International Workshop on Data Science- Present & Future of Open Data & Open Science –PROGRAMME and ABSTRACTS

Mishima Citizens Cultural Hall &Joint Support-Center for Data Science Research,
Mishima, Shizuoka, Japan
12–15 November 2018

Table of Contents :GeneralScope of Workshop, Session Themes ----------------------------------------------

Page
1

Schedule Summary, Local Organization Committee ----------------------------- 2
Advisory Committee, Abbreviation, Website, Contact Address --------------- 3
Programme Summary ------------------------------------------------------------------

4

For Participants, For Presenters ------------------------------------------------------

5

Mishima Citizens Cultural Hall -------------------------------------------------------

6

Registration, Venue & Access ---------------------------------------------------------

7

Accommodation, Social Events -------------------------------------------------------

8

Tour to NIG & DBCLS --------------------------------------------------------------------- 9
NIG Campus Map ------------------------------------------------------------------------

10

Programme ----------------------------------------------------------------------------------- 11

Abstracts -------------------------------------------------------------------------------------- 17

Authors Index -------------------------------------------------------------------------------- 117

International Workshop on Data Science- Present & Future of Open Data & Open Science –Mishima Citizens Cultural Hall &Joint Support-Center for Data Science Research, Mishima, Shizuoka, Japan
12–15 November 2018
Scope of Workshop :The Workshop will focus on recent topics of interest in the field of scientific data, which are
attributed to play a crucial role in accelerating "Open Science" and "Open Data" globally.
Contributions from all scientific disciplines are welcome, including life and bio-science, socialand human science, as well as polar science. Inter-disciplinary orientated topics on data
management are especially encouraged.
A wide range of presentations will be given on topics of effective scientific data management
spanning across the entire spectrum of data management - planning and policy, submission ofprimary and metadata, data sharing for facilitation of inter-disciplinary science, long-termpreservation and stewardship with global and social perspectives.
Topics on industry-academia collaboration, education and capability building on data
sciences, promoting "Open Science" via feedback to the public and archiving are also
encouraged. Contributors will report on successes and challenges recently encountered, bestpractices and experiences learned and what is yet be done to ensure that we leave a data
legacy. Fruitful discussions on data legacy and historical data issues for all branches of science
are expected to give a new proxy for addressing data management issues and to achieve interdisciplinary science linkages.
It is expected that this workshop will lead to mutual understanding of various aspects of databy different stakeholders and it will open new paths for pursuing activities in different fields ofscience. The activities are expected to play a central role in the promotion of inter-disciplinarysciences and new collaborative research paths based on multi-disciplinary data and directly
contribute to global data activities based on the facilities provided by the "Joint Support-Center
for Data Science Research (DS)" of the "Research Organization of Information and Systems(ROIS)".

Session Themes :International data activity: Various data-related aspects of accreditation schemes andtheir benefits, positives and negatives of current approaches of individual international
initiatives, centres and networks, related data management planning, data policy, etc.



National data activity: Various data-related aspects of accreditation schemes and their1benefits, positives and negatives of current approaches of individual national projects,
centres and regional networks, related data management planning, data policy, etc.
Current status of data science: Current status and on-going progress in the field of data
science and related applications for individual disciplines and cross-disciplinary synergies.
This includes a wide range of topics - databases, data systems, metadata schemes,
vocabularies, ontologies, knowledge management, cloud computing, security, storage,
repository practises and standards etc.

Current status of Inter-disciplinary science: Current status and progress in relating to interdisciplinary research activities; data sharing, real-time data handling and manipulation,
virtual observatories, information and communications technology infrastructure protocols
and architectures, sustainability and governance models.



Industry-academia collaboration, education and capability building: Best practise on
industry-academia collaboration, education and capability building in data science, datadriven knowledge transfer, data publication and journals, scientific awards and recognition
schemes.



Legacy data, historical data, future on data science: All aspects of data use evolution,legacy data, historical data and the potentials for enhancing scientific and non-scientificresearch developments through data sharing, citation and publication across disciplines.

Schedule Summary :Monday 12 November 2018; Registration, Public Lecture, Icebreaker Party
Tuesday 13 November 2018; Workshop (day 1), Reception
Wednesday 14 November 2018; Workshop (day 2), Tour to NIG and DBCLS, Banquet
Thursday 15 November 2018; Workshop (day 3)

Local Organization Committee (LOC) : (* Chair)
Masanori Arita (National Institute of Genetics, ROIS)
Tomoya Baba (Data Science Promotion Section, DS, ROIS)
Susumu Goto (Database Center for Life Science, DS, ROIS)
Rue Ikeya (University Research Administrator Station, ROIS)
Akira Kadokura (Polar Environment Data Science Center, DS, ROIS)*Masaki Kanao (Polar Environment Data Science Center, DS, ROIS)
Naoko Kato (Center for Social Data Structuring, DS, ROIS)
Asanobu Kitamoto (Center for Open Data in the Humanities, DS, ROIS)
Tadahiko Maeda (Center for Social Data Structuring, DS, ROIS)
Mari Minowa (Database Center for Life Science, DS, ROIS)
Shinya Nakano (The Institute of Statistical Mathematics, ROIS)
Takeru Nakazato (Database Center for Life Science, DS, ROIS)
Koji Nishimura (Polar Environment Data Science Center, DS, ROIS)
Hideki Noguchi (Center for Genome Informatics, DS, ROIS)
2

Akihiko Nomizu (Data Science Promotion Section, DS, ROIS)
Yoshimasa Tanaka (Polar Environment Data Science Center, DS, ROIS)
Hironori Yabuki (Polar Environment Data Science Center, DS, ROIS)

Advisory Committee (AC): (* Chair)Phillippa Bricher (Australian Antarctic Division)
Taco De Bruin (Royal Netherlands Institute for Sea Research)
Shannon Christoffersen (University of Calgary)
Hiroyuki Enomoto (National Institute of Polar Research, ROIS)*Asao Fujiyama (Joint Support-Center for Data Science Research, ROIS)
Øystein Godøy (Norwegian Meteorological Institute)Kazuhiro Hayashi (National Institute of Science and Technology Policy)
Heidi J. Imker (Illinois University)
Toshihiko Iyemori (Kyoto University)
Yuji Kohara (Database Center for Life Science, DS, ROIS)
Ellsworth LeDrew (University of Waterloo)
Kassim S. Mwitondi (Sheffield Hallam University)
Yasuhiro Murayama (National Institute of Information and Communications Technology)
Tsuneo Odate (National Institute of Polar Research, ROIS)Mark Parsons (Rensselaer Polytechnic Institute)Peter Pulsifer (University of Colorado)
Hideaki Takeda (National Institute of Informatics, ROIS)
Seiji Tsuboi (Japan Agency for Marine-Earth Science and Technology)
Anton Van de Putte (Royal Belgian Institute for Natural Science)
Ryozo Yoshino (Center for Social Data Structuring, DS, ROIS)

Abbreviation :DS: Joint Support-Center for Data Science Research
DBCLS: Database Center for Life Science
DDBJ : DNA Data Bank of Japan
NIG: National Institute of Genetics
ROIS: Research Organization of Information and Systems

Website :https://ds.rois.ac.jp/article/dsws_2018/

Contact Address :data.ws.loc-2018 @ nipr.ac.jp

3

Programme Summary :Monday 12 November 2018
15:30–17:30 Public Lecture (for general public, in Japanese)
16:00–19:30 Registration
18:30–19:30 Icebreaker party @ Mishima Citizens Cultural Hall
Tuesday 13 November 2018
09:05–09:30 Opening Remarks
09:30–12:30 Session A: International and National data activity
12:30–14:00 Group Photo & Lunch
14:00–17:20 Session B1: Data science and Inter-disciplinary science
17:20–18:20 Poster Session
18:30–20:00 Reception @ Mishima Shoukou-Kaigi-Sho

Wednesday 14 November 2018
09:05–11:20 Session B2: Data science and Inter-disciplinary science
11:20–13:20 Session C: Legacy data, Historical data, Industry-academia collaboration
13:20–14:50 Lunch & Poster
14:50–17:50 Visit to NIG & DBCLS
18:00–19:30 Banquet @ NIG Lecture Hall
Thursday 15 November 2018
09:05–12:30 Session D: Education and capability building
12:30–14:00 Lunch & Poster
14:00–17:20 Session E: Future on data science
17:20–17:30 Closing Remarks

4For Participants :Language :The official conference language is English. No simultaneous interpretation service will be
provided.
Session D (Education and capability building, 15 November) will be presented in Japanese, using
slides with English text.

Name Badge :Participants' name badges will be provided at the registration desk of the workshop. All
participants are required to wear the badge throughout the workshop.
Stickers for side events will be pasted to the badges as soon as the payments have been made.
Group Photo:
After the morning session on 13 November (1st day, Session A), all participants are invited totake a "group photo" of the conference. The place is planned in front of the entrance of "Mishima Citizens Cultural Hall ".

WiFi service:
At the venue of "Mishima Citizens Cultural Hall", WiFi service is available. Detail information
about SSID & Password will be given at the registration desk.

For Presenters :Oral Presentation :General presenters are allocated 20 minutes including questions and discussion time.
Keynote speakers are allocated 30 minutes including questions and discussion time.
A Window lap top PC is available for presentations, but presenters can use their own lap tops if
they so wish.
When using the PC of the conference room, please bring presentation file (ppt, pdf) on USB tothe registration desk prior to the beginning of the session.

5

Poster Presentation :Please prepare a poster of the maximum size within the posting board of 1200 mm (width) x
1800 mm (height).
All posters can be posted during three days on 13-15 November 2018, in front of the workshop
main hall (1F, Mishima Citizens Cultural Hall).
Core Times for Poster presentations are allocated as follows;
Tuesday 13 November 17:20–18:20, & Wednesday 14 November 13:50-14:50(for Sessions A, B, C and E)
Thursday 15 November 13:00-14:00 (for Session D)

6

Registration :Registration Desk :Registration desk will open at the following date & time.
On-site registration can be acceptable for each day in front of the workshop main hall (1F,
Mishima Citizens Cultural Hall).
No registration fee is required to attend the conference.
- Monday 12 November 2018
- Tuesday 13 November 2018
- Wednesday 14 November 2018
- Thursday 15 November 2018

16:00-19:30
08:45-18:00
08:45-14:30
08:45-16:00

Pre-Conference Registration :Those who are intending to attend the workshop prior to the conference date can be madefrom the following URL;
https://docs.google.com/forms/d/e/1FAIpQLSc6kNOrCAvXMX0DVA42eFDF67IBpDkzTvfK8A
yQ--tLrRzmWw/viewform
The Organizing Committee add to the participant list & prepare name badges before theconference.

Venue & Access :Conference Venue :Mishima Citizens Cultural Hall (“Mishimashimin Bunka Kaikan”)
Ichiban-cho 20-5, Mishima, Shizuoka, Japan
http://mishima-youyouhall.com/access/
It takes 3 min. southward walk from Mishima JR station to the Citizens Cultural Hall.
The Citizens Cultural Hall is surrounded westward by Mishima Municipal Park ( “Rakujyuen”)
http://www.city.mishima.shizuoka.jp/rakujyu/index.html
Travel Information :From Narita Airport to Mishima JR station:
https://www.nig.ac.jp/nig/pdf/access/Narita-NIG.pdf
From Haneda Airport to Mishima JR station:
https://www.nig.ac.jp/nig/pdf/access/Haneda-NIG.pdf
It takes about 2 hours from both the airports to Mishima.
7

Accommodation :Lunch Service:
There are many restaurants near the Mishima JR station and the Citizens Cultural Hall. Lunch
will not be served at the conference, delegates are advised to use the restaurants within thevicinity of Mishima JR station and the Citizens Cultural Hall.

Hotel Accommodation :As there are several business hotels around Mishima central city area, participants are required
to make their own reservations for accommodation.
The LOC is planning to reserve several rooms at discount rates for invited speakers and foreign
participants in “Mishima Plaza Hotel” located within 6 minutes Walk south of the Mishima
Citizens Cultural Hall.
https://www.mishimaph.co.jp/

Social Events :There will be several social events as follows (public lecture, icebreaker party, banquet,
reception, tour to NIG & DBCLS, etc.).


Public Lecture (12 Nov. 15:30–17:30). The lecture is planned for the general public and it
will be delivered in Japanese at the Mishima Citizens Cultural Hall.
https://ds.rois.ac.jp/post-2635/



Icebreaker party (12 Nov. 18:30–19:30). This will take place at the Mishima Citizens
Cultural Hall, at the cost of 1,000 JPY per delegate. Drinks and light snacks will be served.



Reception (13 Nov. 18:30–20:00). This will take place at the "Mishima Shoukou-Kaigi-Sho(1F TMO Hall)", at the cost of 3,000 JPY per delegate. The TMO Hall locates in front ofMishima Citizens Cultural Hall, at the opposite side of their facing road.

Tour to NIG & DBCLS (14 Nov. afternoon 14:50–17:50). Details are demonstrated inseparated item as below and related map of the NIG campus.

Banquet (14 Nov. 18:00–19:30). To take place at the Lecture Hall (2F of NIG restaurant),delegates will be required to pay 3,000 JPY.

All payments for attending the foregoing events must be made at the registration desk of theconference & at the desks for individual events.

8More detailed information will be announced at the conference venue.

Tour to NIG & DBCLS :Tour to the National Institute of Genetics (NIG)& Database Center for Life Science (DBCLS)(14 November 2018, afternoon 14:50–17:50)
TIME TABLE :14:50 - Participants are expected to gather at the main entrance of the Mishima Citizens
Cultural Hall before this time. Please choose the transportation route either using A)
public bus transportation or B) shuttle bus service of NIG.
15:00 - Take a public bus transportation ("City bus") to NIG at the bus stop 1 min. southward of
the Mishima Citizens Cultural Hall (250 JPY for each half way ride). It takes about 15 min.
to NIG. The delegates who are not in time to take the public bus, please use a Taxi to go
to NIG.
- 15:30 Arrive at the entrance of NIG. Depending on the number of people, participants will be
divided into one or two group(s) & migrate the following five "SPOT" places inside thecampus.

15:30 - SPOT 1: General introduction of NIG at the "NIG Museum Exhibition Hall" besides theentrance (by Project Associate Prof. Mitsuhiko Kurusu, Office for Research
Development).
16:00 - SPOT 2: Introduction of Advanced Genomics Center at the Computer Building (West-1F)(by Prof. Ken Kurokawa, Director of Advanced Genomics Center).
16:30 - SPOT 3: Introduction of Center for Genome Informatics & DBCLS at the Computer
Building (West-3F) (by Prof. Hideki Noguchi, Director of Center for Genome Informatics;& Dr. Takeru Nakazato of DBCLS).
17:00 - SPOT 4: Introduction of the DNA Data Bank of Japan (DDBJ) at the Computer Building(East-4F) (by Prof. Masanori Arita, Director of DDBJ center).
17:30 - SPOT 5: Introduction of Drosophila Stock Center (by Prof. Kuniaki Saito, Director of
Invertebrate Genetics Laboratory).
18:00 - Banquet: take place at the Lecture Hall (2F of NIG restaurant), delegates will be required
to pay 3,000 JPY.

9

17:46, or 19:06, or 20:36 - Take a public bus transportation ("City bus") to the southern area
near Mishima JR Station, from the road just outside NIG (250 JPY for each half way ride,
It takes about 15 min.).
Detail timetable from NIG to JR Mishima Station can be checked in ;https://www.nig.ac.jp/nig/pdf/access/busE-NIG.pdf

10International Workshop on Data Science- Present & Future of Open Data & Open Science –PROGRAMME

Mishima Citizens Cultural Hall &Joint Support-Center for Data Science Research,
Mishima, Shizuoka, Japan
12–15 November 2018

PROGRAMMEInternational Workshop on Data Science- Present & Future of Open Data & Open Science –12 – 15 November 2018

Mishima Citizens Cultural Hall & Joint Support-Center for Data Science Research,
Mishima, Shizuoka, Japan
Monday 12 November 2018

15:30–17:30 Public Lecture (for general public, in Japanese)
16:00–19:30 Registration @ Mishima Citizens Cultural Hall
18:30–19:30 Icebreaker party @ Mishima Citizens Cultural Hall (Facilitator: Tomoya Baba)• Welcome Remark: Takuji Nakamura (Director-General, National Institute of Polar Research, ROIS)

Tuesday 13 November 2018

09:05–09:30 Opening Remarks (Chair: Masaki Kanao)• Asao Fujiyama (Director-General, Joint Support-Center for Data Science Research (DS), ROIS) (15’)• Masaki Kanao (LOC Chair), agenda of workshop & practical information (10’)
09:30–12:30 Session A: International and National data activity(Session chair: Akira Kadokura, Seiji Tsuboi)
Keynote 1: The International Seismological Centre (ISC): 50-Year Miracle
Dmitry A. Storchak (International Seismological Centre) (30’)• Data Management at Korea Polar Data Center, Korea Polar Research InstituteDongchan Joo (Korea Polar Research Institute) (20’)• Activities of World Data Center for Geomagnetism, Kyoto
Satoshi Taguchi (Data Analysis Center for Geomagnetism and Space Magnetism,
Kyoto University) (20’)• Coffee Break (20’)
Keynote 2: World Data Center for Microorganisms: The global cooperation on microbial big data
Juncai Ma (Chinese Academy of Sciences) (30’)• DIAS Platform Contributing to Open Science in Earth Environmental Informatics
Masaki Yasukawa (Earth Observation Data Integration and Fusion Research Initiative,
the University of Tokyo) (20’)• Data Management at Polar Research Institute of China
Lizong Wu (Polar Research Institute of China) (20’)• Activities of Polar Environment Data Science Center
Akira Kadokura (Polar Environment Data Science Center, DS, ROIS) (20’)• Discussion
1112:30–14:00 Group Photo & Lunch
14:00–17:20 Session B1: Data science and Inter-disciplinary science(Session chair: Susumu Goto, Takashi Watanabe)
Keynote 1: Amenability of the United Nation’s Sustainable Development Goals to Big Data Modelling
Kassim S. Mwitondi (Sheffield Hallam University) (30’)• Life Science Database Integration and Its Application for Medical Science
Susumu Goto (Database Center for Life Science, DS, ROIS) (20’)• Current Situation of Data Archiving for Japanese Official Statistics
Shinsuke Ito (Chuo University) (20’)• Importance of semantics and rated challenges in science and humanities according to the Humboldtian
ideal
Bernd Ritschel (Kyoto University) (20’)•

Coffee Break (20’)

Keynote 2: SPEDAS (Space Physics Environment Data Analysis Software): Multi-mission Heliophysics DataManagement, Analysis, Visualization, and Collaboration
Jim Lewis (Space Sciences Laboratory, University of California) (30’)• Application of deep learning and large scale simulation to the Earth science
Seiji Tsuboi (Japan Agency for Marine-Earth Science and Technology) (20’)• Domestic and international activities of DOI-minting to solar- terrestrial physics data and their citationin publication
Masahito Nose (Nagoya University) (20’)• Multidisciplinary Study of the Earth’s Environment in 18th-19th Centuries - A Trial to find an Approach to
the Open Data and Open Science
Takashi Watanabe (International Program Office, ICSU World Data System) (20’)• Discussion
17:20–18:20 Poster Session
18:30–20:00 Reception @ Mishima Shoukou-Kaigi-Sho (Facilitator: Masaki Kanao)•

Welcome Remark: Ryoichi Fujii (President, Research Organization of Information and Systems (ROIS))

Wednesday 14 November 2018

09:05–11:20 Session B2: Data science and Inter-disciplinary science(Session chair: Hideaki Takeda, Masaki Kanao)
Keynote 3: Data Archives for Social Science in Korea, Challenges and Opportunities
Hearan Koo (Seoul National University) (30’)• Exploring public attitudes toward scientific research with visitor surveys and nationally representative
surveys
Naoko Kato-Nitta (Center for Social Data Structuring, DS, ROIS) (20’)
Keynote 4: Age-Period-Cohort Analysis of Data Obtained from Repeated Social Surveys
Such as the Surveys on the Japanese National Character
Takashi Nakamura (Center for Social Data Structuring, DS, ROIS) (30’)
12

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•
•

Extended Cell Suppression Problem Towards Better Data
Kazuhiro Minami (The Institute of Statistical Mathematics, ROIS) (20’)
Estimation of age-specific reporting ratio of sentinel influenza surveillance using seroprevalence data
Masaya Saito (The Institute of Statistical Mathematics, ROIS) (20’)
Coffee Break (15’)

11:20–13:20 Session C: Legacy data, Historical data, Industry-academia collaboration(Session chair: Koji Nishimura, Naoko Kato-Nitta)
Keynote 1: The ISC-GEM Global Earthquake Catalogue: Making Good Use of Historical Observations
Dmitry A. Storchak (International Seismological Centre) (30’)• Reframing Scholarly Communication by Persistent Identifier
Hideaki Takeda (National Institute of Informatics, ROIS) (20’)
Keynote 2: Resource and Environment Scientific Data Sharing and Disaster Risk Reduction Knowledge Service
Juanle Wang (Chinese Academy of Sciences) (30’)• Data Treatment Strategies and Some Science Projects of PANSY Rader
Koji Nishimura (Polar Environment Data Science Center, DS, ROIS) (20’)• Research Data Management in Industry-Academia Collaboration
Yuko Toda and Hodaka Nakanishi (Teikyo University) (20’)• Discussion
13:20–14:50 Lunch & Poster
14:50–17:50 Visit to NIG & DBCLS
18:00–19:30 Banquet @ NIG Lecture Hall (Facilitator: Hideki Noguchi)•

Welcome Remark: Isao Katsura (Director-General, National Institute of Genetics, ROIS)

Thursday 15 November 2018

09:05–12:30 Session D: Education and capability building (presented by Japanese, using English slides)(Session chair: Masanori Arita, Yoshimasa Tanaka)
Keynote 1: Health - Prediction of Infectious Disease System Dynamics using
Machine Learning and Mathematics
Shailza Singh (National Centre for Cell Science, India) (30’)• Open genome analysis in the post-genomic era
Masanori Arita (National Institute of Genetics, ROIS) (20’)• Education Programs for AI/IoT/BigData Skills Development at the Medical and Drug Discovery Data
Science Consortium
Eli Kaminuma and Hiroshi Tanaka (Tokyo Medical and Dental University) (20’)• Kyoto University Academic Data Innovation Unit - bottom-up promotion for various research data
infrastructure
Takaaki Aoki and Shoji Kajita (Kyoto University) (20’)•

Coffee Break (15’)

Keynote 2: From Human Genome Project to Genome Cohort Study
13

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•
•

Atsushi Shimizu (Iwate Tohoku Medical Megabank) (30’)
Genetic relationship between tea plant (Camellia sinensis) and ornamental camellia (C. japonica)
Kazumi Furukawa (National Institute of Technology, Numazu College) (20’)
Research outline with regional theme: Examples using machine learning, geographic information
system, and social big data
Shizuo Suzuki (National Institute of Technology, Numazu College) (20’)
Useful Tools for Education and Capacity Building about Solar Terrestrial Physics Study
Yoshimasa Tanaka (Polar Environment Data Science Center, DS, ROIS) (20’)
Panel discussion (10’)

12:30–14:00 Lunch & Poster
14:00–17:20 Session E: Future on data science(Session chair: Asanobu Kitamoto, Hironori Yabuki)

Keynote 1: Embedding Machine Learning in the Data Life Cycle - An Example from Minerals Exploration
Jens Klump (Mineral Resources, CSIRO in Australia) (30’)• Toward universal information access on the digital object cloud
Kei Kurakawa (National Institute of Informatics, ROIS) (20’)• Development of Japanese Research Data Discovery Service
Fumihiro Kato (National Institute of Informatics, ROIS) (20’)• AI and Data Science - Machine Learning as Digital Catalyst for Data Curation
Asanobu Kitamoto (Center for Open Data in the Humanities, DS, ROIS) (20’)•

Coffee Break (20’)

Keynote 2: Open Research Data Progress in Korea
Myung-Seok Choi (Korea Institute of Science and Technology Information) (30’)• The Cooking Recipes without Border Data set: FAIR challenges
Frederic Andres (National Institute of Informatics, ROIS) (20’)• Arctic Data Archive System (ADS)
Hironori Yabuki (Polar Environment Data Science Center, DS, ROIS) (20’)• Perspective of Open Science in Japan driven by Integrated Innovation Strategy
Kazuhiro Hayashi (National Institute of Science and Technology Policy) (20’)•

Discussion

17:20–17:30 Closing Remarks (LOC Chair)

14

Poster Presentation @ outside the Conference Hall

Core Time 1; Tuesday 13 November 17:20–18:20, Wednesday 14 November 13:50-14:50
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•
•
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•

P-1: Multivariate analysis of the occupations of rental rooms by using the housing information website
data
Hayafumi Watanabe (Center for Social Data Structuring, DS, ROIS)
P-2: Useful Tools for Education and Capacity Building about Solar Terrestrial Physics Study
Yoshimasa Tanaka (Polar Environment Data Science Center, DS, ROIS)
P-3: Arctic Data Archive System (ADS)
Hironori Yabuki (Polar Environment Data Science Center, DS, ROIS)
P-4: Data Processing and Archive System for the Antarctic PANSY Rader
Koji Nishimura (Polar Environment Data Science Center, DS, ROIS)
P-5: "Polar Data Journal" : A new data publishing platform for polar science
Akira Kadokura (Polar Environment Data Science Center, DS, ROIS)
P-6: A decade of history for polar data management in Japan
Masaki Kanao (Polar Environment Data Science Center, DS, ROIS)
P-7: Data and metadata sharing among AFoPS countries
Masaki Kanao (Polar Environment Data Science Center, DS, ROIS)
P-8: Development of Metadata, Conversion and Archiving of the Time Series Data of the Completed
Censuses and Surveys of the BBS
Chandra Shekhar Roy (Bangladesh Bureau of Statistics)
P-9: An attempt for the thermal transport modelling of fusion plasmas based on the statistical approach
Masayuki Yokoyama (National Institute for Fusion Science)Core Time 2; Thursday 15 November 13:00-14:00 (for Session D)
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P-10: A Python library for parallelised particle filters
Shinya Nakano (The Institute of Statistical Mathematics, ROIS)
P-11: A Study on Speaker Discrimination Using Machine Learning
Wataru Murata and ManYong Jeong (National Institute of Technology, Numazu College)
P-12: A Study on Speaker Discrimination by Deep Learning
Shinpei Mine and ManYong Jeong (National Institute of Technology, Numazu College)
P-13: A Study on Tapping Diagnosis of Structures Using Machine Learning
Ken Higuchi and ManYong Jeong (National Institute of Technology, Numazu College)
P-14: Development of Traffic Flow Data Measurement System by Drive Recorder
Hironori Kanazawa and ManYong Jeong (National Institute of Technology, Numazu College)
P-15: Optimization of Pipe Support Arrangement by Machine Learning
Hisashi Sakashita and ManYong Jeong (National Institute of Technology, Numazu College)
P-16: Genomic analysis in somatic embryogenesis of tea plant(Camellia sinensis)
Kaito Yamaki and Kazumi Furukawa (National Institute of Technology, Numazu College)
P-17: Preliminary estimation of the aesthetic value of cultural ecosystem services by mapping
geotagged photos from social media data around Mt. Fuji and Izu Peninsula
Naoto Unno and Shizuo Suzuki (National Institute of Technology, Numazu College)
P-18: Preliminary classification of Japanese cedar forests with aerial photographs using machinelearning approach
Ochiai Ryosuke and Shizuo Suzuki (National Institute of Technology, Numazu College)
P-19: Preliminary estimation of tea leaf quality using remote sensing techniques in Numazu city15

•Tomoaki Okatsu and Shizuo Suzuki (National Institute of Technology, Numazu College)
P-20: Preliminary application of results in Japanese historical character recognition by machine learning
to locally historical documents
Masuhiro Yamaji and Shizuo Suzuki (National Institute of Technology, Numazu College)

16International Workshop on Data Science- Present & Future of Open Data & Open Science –ABSTRACTS

Mishima Citizens Cultural Hall &Joint Support-Center for Data Science Research,
Mishima, Shizuoka, Japan
12–15 November 2018

The Cooking Recipes without Border Data set: FAIR challenges
Frederic Andres1*, Laurent D’Orazio2, Johyn Papin3, William I. Grosky 4
1*

NII, Chiyoda-ku, Tokyo, Japan
2

IRISA, Lannion, France
University of Rennes, Lannion, France
4
University of Michigan-Dearborn, Michigan, USA
Email: andres@nii.ac.jp
3

Summary. The following is a set of guidelines for preparing an extended abstract submission for the "IOneof the grand challenges of Open Data and Open Science is to facilitate knowledge discovery and sharing bycollaboration between human and machines. We review how we apply the standard FAIR referencearchitecture for Big Data Governance and Metadata Management to the CRWB1 dataset that is scalable. We
review how the dataset supports the Findability, Accessibility, Interoperability, and Reusability withoutworrying about the ingestion of new data source and different structures. One challenge is to enable data
integration/mashup among heterogeneous food/cooking recipes dataset and make data discoverable,
accessible, and usable through a machine readable and actionable standard data infrastructure.
Keywords. Food, Cooking recipes, FAIR data model, Collective Intelligence.

1. Introduction

generation, cooking execution plan optimization,

Food science is the study of the physical,

recipe DNA coding, recipe similarity), findcorrelations between cooking recipe, nutrition

biological, and chemical makeup of food; and theissues, and food tasting, or look into whateverconcepts underlying food processing. Recipes are

topic they are interested in.

perhaps the simplest examples of the connection
between food science and cooking: Cooking is

3. A FAIR Data Set

chemistry and Physics as there is a fusion of
Cooking

Science,andmeasurements,

ingredients

instructions

and

and

The FAIR Model [1] is a set of guiding principles towrittenmake data Findable, Accessible, Interoperable,

documentation, all designed to lead anyone to a

and Reusable.

specific, repeatable outcome that someone else

The CRWB dataset is Findable: (1) its metadata

has also perfected.

and data have been assigned a globally uniqueand eternally persistent identifier; (2) it is

2. CRWB Data Set

described with rich metadata; (3) its metadata are

12

The CRWB data Set is a huge linked open dataindexed in a searchable service; and (4) its

collection [2] of 60 years collection of cooking

metadata specify the CRWB identifier.

recipes and a CRWBvoc set of ontologies

The CRWB dataset is Accessible: (1) its

includingrecipemetadata and data are retrievable by theirtaxonomy, a cooking process-centric ontology,

identifier using a standardized CRWB protocol. (2)and a food tasting ontology. This allows

this CRWB protocol is open, free, and universally

accessibility to other researchers who want toimplementable; (3) this CRWB protocol (e.g. [3])investigate

allows for an authentication and authorization

aniningredients

severalontology,

fields(dishrecipeprocedure, where necessary and (4) its metadata1

are accessible, even when the data are no longer

https://github.com/fredericandres/CRWB-ResearchGroup/wiki/1)-CRWB-RSbench-Introduction
2
Cooking Recipes Without Border Dataset

available.

17

The CRWB dataset is Interoperable: (1) the
CRWB (meta)data use a formal, accessible, shared,

6. Big Data Mashup Challenges

and broadly applicable language for knowledge

The CRWB data will help to ease the Big Data

representation (see the CRWB data structure in

Mashup including IoT mashup tools as theFig 1); (2) the CRWB (meta)data use the CRWBvoc

integration of heterogeneous cooking recipes

ontology that follow FAIR principles; and (3) theand applications from multiple sources including

CRWB metadata and data include qualified

IoT for health care and other research purpose.

references to other (meta)data.
The CRWB dataset is Re-usable: (1) the CRWB

7. Conclusions

metadata and data have a plurality of accurate

The CRWB dataset follows the FAIR model forand relevant attributes; (2) (meta)data areopen

released with a clear and accessible data usage
and

(4)

(meta)data

and

is

used

in

the

dedicated to the evaluation of new algorithms

meet

and

food/cooking recipe domain-relevant community

technologies

forcookingreciperecommendation, access, and exploration. It

standards.

4. Bigsciencebenchmarking initiative of CRWB2 research grouplicense; (3) (meta)data are associated with theirprovenance;

data

offers tasks that are related to human and social

Data

Governanceaspects of cooking recipe and healthy eating

Mgt

decision-making to the research community.

Challenges
Publicly available cooking recipe data and their

Acknowledgments. We thank the NII for its

associatednutritional

support to the CRWB project.

information

are

and

generated

health-related
from

multipleorganizations, each with their own methodology

Referencesof data collection and dissemination. Mechanisms

1.

and rights for accessing enhanced cooking recipe
data from such diversified organizations can bechallenging.

2.

5. Big Data Metadata Mgt Challenges
Cooking recipe collections as raw data or as
linked open data have different metadata
structures from different data hubs. Promoting

3.

interoperable semantic meaning and syntactic
structures of these data fields can be challenging.
The CRWB data will help to ease the Big Data
Mashup including IoT mashup tools as theintegration of heterogeneous cooking recipes
and applications from multiple sources including
IoT for health care and other research purposes.

18

Wilkinson, M. D., et al., The FAIR Guiding
Principles for scientific data management
and stewardship. Scientific Data, 3, 15 March
2016, doi:10.1038/sdata.2016.18, retrieved
online on 21 June 2016
Andres, F., The CRWB RSbench: Towards a
Cooking Recipe Benchmark Initiative. Data
Engineering Meets Intelligent Food andCooking Recipe Workshop (DECOR 2018),
ICDE 2018, CNAM, Paris, France; 04/2018,
DOI:10.1109/ICDEW.2018.00032, 2018
Papin, J., Andres, F., D'Orazio, L., A Method tobuild a Geolocalized Food Price Time Series
Knowledge Base analyzable by Everyone.
LADaS - Latin American Data Science
Workshop 2018, VLDB workshop 2018., Riode Janeiro, Brazil; 08/2018

Fig 1: Example of the CRWB data structure

19

Kyoto University Academic Data Innovation Unit - bottom-up
promotion for various research data infrastructure
Takaaki Aoki 1*, Shoji Kajita 1
1* Kyoto University, Sakyo Kyoto 606-8501, Japan
Email: aoki.takaaki.6v@kyoto-u.ac.jp

Summary. Kyoto University has established the “Academic Data Innovation Unit” in November 2017. The
goal of this unit (known as “Kudzu Unit”) is the bottom-up promotion of open data science, which is initiatedin various scientific fields. In this paper, the background, motivation and current activities of the Kudzu unit
are reviewed.
Keywords. Interdisciplinary collaboration, research data management, academic research data.

1. Research data openness promoted

Kyoto University (KU) is the second largest
national university and has been leading theby the Japanese Government

openness of academic knowledge. For example,

From the view of Japanese academia, thethe KU library is the first one that adopted theopenness of research data has been discussed

open access policy on research papers. A large

based on two aspects. One is the strengthening

number of departments and research institutesof research fairness or integrity. Around 2014,

have a vast amount of research databases

Japanese academia was shocked by high-profile
incidentsofscientificmisconduct.

accessible to the public. Based on the context ofJapan’s

research integrity, KU has developed rules andMinistry of Education, Culture, Sports, Scienceorganized systems to preserve research data [4]and Technology (MEXT) revised the old previous
guideline

againstmisconductandto prove to the integrity of research publications.

issuedThese actions for enforcement of research“Guidelines for Responding to Misconduct inintegrity proceeded in a top-down scheme. On

Research” [1], which mandates that researchers

the other hand, it is considered that policy andshould preserve research data for a certain period

system development for open research data at

and disclose it upon request. The second aspect isuniversity initiated in a bottom-up project. The

the promotion of open science. The term "openacademic research data at KU are very diverse,

research data" firstly showed in A joint statement

whichby the G8 Science Ministers in 2013. Then, the

means

that

the

developmentofcomprehensive rules or systems covers all

Japanese politics and business establishments

research filed is not possible.

issued several guidelines and statements topromote open science and initiate innovation [2].

3. The academic data innovation unit

Especially in June 2018, Japanese cabinet office

The academic data innovation unit (known as

issued a guideline for Japanese research and"Kudzu Unit") is organized in November 2017

development agencies to establish data policy tounder the "Center for the Promotion oflead open science activities in each scientific field[3].

Interdisciplinary Education and Research" of KU.

2. Open research activities at Kyoto

policy and system development for university-

The mission of the unit is a preliminary study ofwide research data management, which includes

University

the acquisition, preservation, analyze, sharing,20

and publication of research data. Unit members

References

consist of various stakeholders around research1.

data, which includes not only the faculties or
researchers

engagedwiththeacademic

databases but including the staffs of theuniversity
administration

library,
office,museum,
and

IT

2.

research
management

3.

division.
The main activity of the Kudzu unit is toorganize workshops annually. "The 5th open

4.

science data workshop" is held, on March 2018.
The previous series of the workshop has beenarranged by Kyoto University Geomagnetism

5.

Center, which is pioneering open data science
activities in geoscience. Kudzu unit took over the
management and framework of the workshop,
which gathers the reports on the current progressof activities of open data science in various fields.
The next workshop will be scheduled in October
2018,

which

focusesonresearchdata

management (RDM) maturity assessment. The
RDM rubric has been proposed by the California
Digital Library [5]. It defines the 6 phases in
research data lifecycle and 4 degrees of maturity
for each phase. The participants of the workshops,
mainly from KU, will evaluate the status of theirresearch data management rules. The output of
the workshop is expected that the participants
will learn the standard method of RDM andperspective view on it, while the organizer will
make a comprehensive map of open data science
activities in KU, which will be referenced bymaking the roadmap of university-wide open
science initiatives.

4. Conclusion
The Kudzu unit is established in order tounderstand the current status and develop thefuture plan of research data management in theuniversity. The work of the unit is expected tofoster appropriate research data management
manner applicated to both research integrity andopen science promotion.

21

Ministry of Education, Culture, Sports,
ScienceandTechnology.
http://www.mext.go.jp/a_menu/jinzai/fusei/
1359618.htm
National
InstituteforInformatics,
https://rcos.nii.ac.jp/en/openscience/internal/
Cabinet Office, Government of Japan,
http://www8.cao.go.jp/cstp/stsonota/datapo
licy/datapolicy.html
Kyoto University, http://www.kyotou.ac.jp/en/research/ethic/research_guide/do
cuments/research-integrityregulations201503.pdf [10-Apr-2017]
California
DigitalLibrary,
https://uc3.cdlib.org/2018/01/11/supportyour-data/

Open genome analysis in the post-genomic era
Masanori Arita 1* , 2
1

2* National Institute of Genetics, Yata 1111, Mishima, Shizuoka, 411-8540, JapanRIKEN Centre for Sustainable Resource Science, Suehiro 1-7-22, Tsurumi, Kanagawa, 230-0045, Japan
Email: arita@nig.ac.jp

Summary. The cost of DNA sequencing is ever decreasing; non-human sequences will soon become open
commodity data like weather information, whereas human sequences become access-restricted, proprietarydata like personal photographs. In this setting, the reference frames of representative genomes become
more important to map and understand (possibly) streaming big data. To support the requirement of highly
computational, interdisciplinary bioscience, we need to design a tiered and federated data warehousing
scheme that offers reliable genome references and study contents.
Keywords. DDBJ, genome, warehousing.

1. Size of genome data in 2025

vendors [4]. The responsibility of data storage andThe

distribution will be also delegated to partner

advancement

of

DNA

sequencing

repositories, and the main focus of the registrar

technologies surpasses that of computer chips

becomes the coordination (or standardization) ofand storages; the cost of sequencing has

analysis frameworks. The integration of biologicaldrastically dropped in the last decade (i.e. next-

domain expertise is the most expensive part, andgeneration sequencing or NGS), and the latest

for this reason, commercial sectors expect

instrument, Illumina NovaSeq 6000, can output 6

governments or national sectors to solve thisTera bases within 2 days [1]. Such data cannot be

problem. In exchange, software platforms ortransferred to or accumulated in a single,

predictions will be provided by data sciencecentralized warehouse, such as the International

companies such as Google and Amazon [5],Nucleotide Sequence Database Collaboration(INSDC)betweenNational

Centertargeting academic researchers as their major

forcustomers.

Biotechnology Information (NCBI in the UnitedStates), European Bioinformatics Institute (EBI),
and DNA Data Bank of Japan (DDBJ). Indeed,

3. Importance of reference frames

Illumina Inc. extrapolates that the current

Human genomes are actively analysed in various

sequencing ability already surpasses 35 Peta

ways such as RNA-Seq, Methyl-Seq, or ChIP-Seq.

bases per year, and required storage size reaches

All these technologies presuppose the availability

Exabytesupportof reference genomes. Analysis of streaming databroadband access to such data, the cloud-for virus sensors or environmental monitoringscale

by 2025[2,3]. To

computing system seems to be the only practicalalso requires reliable references. Such templates

solution.

should be open, citable, and versioned through apublic community, such as INSDC.

2. Roles of centralized repositories
At least for 10 years, INSDC will remain as the

4. Education is the crucial part

uniquedata

registrar/coordinator;

physicalTo fully exploit the fruits of new era, students

however,

inevitably

should learn computer science in addition tofederated, e.g., with BGI-Online and sequencer

modern biology. Early exposure is the key just as

repositories,

become22

younger kids can learn natural languages fasterandbetter.

At

DDBJ

we

provide2.

onlinecoursework for different levels to support active,

3.

voluntary learners.

4.

Acknowledgments. This work is supported by
AMED-CREST (JP18gm0910005, JP18gm1010006),
and the National Bioscience Database Center of
JST.

5.

References1.

Illumina Inc. NovaSeq System (data sheet),https://www.illumina.com/systems/sequenci
ng-platforms/novaseq.html [accessed on: Oct
2018]

23

Stephens, ZD, Lee, SY, Faghri, F et al., Big
Data: Astronomical or Genomical? PLOS
Biology, 13(7), e1002195
Schatz MC, Biological data sciences ingenome research. Genome Res 25, 1417-1422,
2015
BGI
OnlineforBig
Data
https://www.bgionline.com/;Basespace Sequence Hub by Illumina Inc.
https://www.illumina.com/products/bytype/informatics-products/basespacesequence-hub.html [accessed on: Oct 2018]
Google Genomics in Google Cloud
https://cloud.google.com/genomics/;
Amazon Web Service (AWS) Genomics
https://aws.amazon.com/jp/health/genomics/ [accessed on: Oct 2018]

Open Research Data Progress in Korea
Myung-Seok Choi 1*, Sanghwan Lee1
1

* Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea
Email: mschoi@kisti.re.kr

Summary. As the advent of the Digital Era and data-driven research paradigm, Open Science, based on
openness and sharing of research results, has emerged as a global agenda for scientific research. National
policies for sharing and re-use of research data from publicly-funded research are in effect globally. In Korea,
it is just getting started to build policies and infrastructure for open research data. In this paper, weinvestigate the current status of research data sharing and utilization in Korea. And then we briefly introduce
a recent national strategic plan for open research data and related pilot projects.
Keywords. Open Science, Open Research Data, Research Data Management, Data Management Plan.

1. Introduction

essential mechanism to deter, detect and stamp

With the advance of digital technology, such as

out bad science as well as to enable scientificdiscovery by assuring transparency in scientificadvanced research equipment, sensors, and data

research[3]. Recently, there are growing concerns

processing technology, large-scale research dataover the reproducibility crisis in scientific

has been explosively produced, and a data-

research[6]. In order to verify the reproducibility

centric fourth research paradigm has emerged[1].

of the research, it is necessary to disclose the data

Moreover, R&D trends that emphasizes sharing

produced and used in the research. In this paper,and convergence are expanding to Open Science

we introduce the current status and future

paradigm for easy access and utilization of

direction of open research data in Korea.

research results from publicly-funded research
projects. OECD has adopted Open Science as one

2. Status and Issues of Open Researchof major policy agendas, and many countries are

Data in Korea

promoting policies for the spread of open scienceand focusing on developing infrastructures[2-5].

In Korea, national R&D information has been

Open Science movement is expanding from

integrated

open access of research publications to open

andmanagedthrough

NTIS(http://www.ntis.go.kr). While there is a

research data which allows easy access and reusesharingof research data produced in the process ofandutilization

systemofpublicgovernment data and a limited range of researchscientific research[2]. It is acting as the catalyst for

data, the legal basis and related infrastructures

data-driven collaborative research for global

are still insufficient for accepting diverse research

problem solving. In many countries including

data as the first-class outcome of national R&D

USA, UK and Australia, open research data

projects and sharing and utilizing them atpolicies including data management plan(DMP)

national level.

have been implemented to create value throughVarious types and sizes of reusable researchsystematic management, easy access and reusedata are being produced from national R&D

of research data from publicly-funded research

projects. However, these data are stored andprojects [4 -5].

managedOpenness is the basic premise for self-

at

the

individual

researcherorlaboratory level, and partly shared by personalcorrection, the most important feature of

request. This is because data construction itself isscientific research, to work and is also the24•

not recognized as major research achievements,
and additional work related to data management

Developing a pilot system of national
research data platform•

and quality‧responsibility issues are recognized

Performing pilot projects for disciplinary data
centers such as bioinformatics, materials,

as a burden to most researchers.

artificial intelligence, and large researchequipment.

3. National Strategic Plan for Open•

Research Data

Developing a data repository system forresearch institutes

As data-driven R&D trends increase demand forresearch data utilization, Ministry of Science and4. Conclusions

ICT of Korea established a research data sharing‧

Promoting sharing and utilization of researchutilization strategy in January 2018 in order todata from publicly funded research is becoming a

construct a national system in which researchers

global agenda to prepare for a data-driven

can easily share and utilize research data
accumulated through national R&D projects.

research paradigm and is also an essential(Vision) Knowledge-assetization‧big-dataization

productivity of research. Open research data iselement

establishingbyrevising

hierarchicallegalsystem,researchdata

perspective.

theshouldresearchsuch

1.

Establishment of legal basis for nation-wide
management ‧ sharing ‧2.

utilization•

Promotion of industrial utilization of researchdata and job creation

3.

Currently action plans for pursuing the4.

strategy are being developed and several pilot
projects are underway this year.
•

Organizing a research data network forcollaboration

andconsensus

5.

building

between various stakeholders
•

able

to

spreadtoindividual

as

data

producers,

data

scientists,

References

utilization experts
data

be

research data.

Support of training data and computing

researchdata

researchers, and funders is essential for open

Development of a national research data
platform•

national

close collaboration between various stakeholders

data sharing communities

•

all,

researchers' data sharing culture. Furthermore,datamanagement system and encouragement of•ofcenters can let data utilization increase. At last, it(Key tasks)
of

First

preceded. Then, fostering disciplinary data

fostering data science experts
Establishmentandinfrastructures as well as legal basis should bemanagement system and infrastructures, and•transparency

so it should be approached from a long-term(Goal) Enabling national research data sharing
utilization

improvingaccompanied by transforming research culture,of research data
and

for

6.

Revising legal system for mandating data
management plan25

Hey, T., TanSley, S., Tolle, K., The Fourth
Paradigm: Data-Intensive Scientific Discovery.
Microsoft Research, 2009
OECD, Making Open Science a Reality. OECD
Science, Technology and Industry Policy Papers,
25, 2015
The Royal Society, Science as an open
enterprise, The Royal Society Science Policy
Centre Report, 2012
Holdren, J., Increasing Access to the Resultsof Federally Funded Scientific Research.
White House OSTP Memorandum, 2013
Open Research Data Forum, Concordat on
Open Research Data. Open Research Data
Forum Report, 2016
Baker, M., Is There a Reproducibility Crisis?.
Nature, 533(7604), 452-454, 2016

Genetic relationship between tea plant (Camellia sinensis) andornamental camellia (C. japonica)
Kazumi Furukawa1*
1* National Institute of Technology, Numazu College, Ooka 3600 Numazu, Shizuoka, 410-8501, Japan
Email: furukawa@numazu-ct.ac.jp

Summary. For the breeding of tea plant (Camellia sinensis, 2n=30), we investigated the chromosome
structure and partial sequence of the tea caffeine synthase (TCS1) gene among the Camellia species used as
the genetic resources of tea. A fluorescence in situ hybridization assay showed that the shapes of thechromosomes having 5S rDNA were similar between C. sinensis and C. japonica (2n=30). Moreover, bivalents
were observed in the interspecific hybrid of the two Camellia species. Thus, because these species havehomoeologous chromosomes, we could infer the extent of their genetic relationship. In the interspecifichybrid, a strongly methylated cytosine was detected in one chromosome. This allowed us to consider
changes in epigenetic traits during the formation of hybrids. In addition, the diversity of the caffeine
synthetic ability was found unique to the tea plants of the Camellia genus. As a result, an in/del of 83 bp was
detected in the intron of the partial sequence of TCS1, and several mutations were detected in C. sinensis. In
C. japonica, caffeine was detected in extremely small amounts, and it was thought that such a difference inthe arrangement of nucleotides causes different traits. These mutations were considered important forinterspecific hybridization in Camellia species.
Keywords: tea plant, Camellia sinensis, Camellia japonica, chromosome analysis, 5-methylated cytosine.

1. Introduction

nou 1gou (C. sinensis ×C. japonica)

The leaves of Camellia sinensis have long been

as comparison plants.

were used

used as tea throughout the world. There are

3. Chromosome analysis

several closely related species in the genus
1

Camellia . Ornamental camellia (C. japonica) is

A 5S rDNA probe labelled with fluorescein

one of the genetic resources for tea breeding

isothiocyanate (FITC) or indocarbocyanine (CY3)since it has cold tolerance and caffeine-less trait.

was hybridized to theBoth C. sinensis and C. japonica have the samedetected by their fluorescence intensities2. Twochromosomes andnumber of chromosomes (2n=30), and thesignals were detected on each chromosome of C.

interspecific hybridization between them issinensis and C. japonica (Fig. 1 top and middle). In

possible at low rates. To elucidate the efficiency

C. sasanqua, several signals were detected (Fig. 1).

of hybrid formation between these species, we

DAPI

investigated the morphological comparison ofchromosomes and the diversity of the tea
C. sinensis(2n=30)caffeine synthase (TCS1) genes in Camellia species.

2. Materials
A Japanese green tea cultivar Sayamakaori was

C. japonica(2n=30)used as the tea plant material in this study. The
ornamental camellia interspecific hybrids Robiraki(C. japonica ×C. sinensis) and Cha chukanbohon

26

C. sasanqua(7x=105)

5S rDNA

Merge

signal was detected on the chromosomes of theFig. 1 Fluorescence in situ hybridization images of the
5S rDNA of the mitotic chromosomes of three
Camellia species. Arrows indicate the 5S rDNA signals.

parental plants. We need further investigation on
this issue, but it will be interesting to identify

The lengths of chromosomes and the density of

uniparental chromosomes showing epigenetic

4′,6-diamidino-2-phenylindole (DAPI) along theirregulation.

lengths were measured in C. sinensis and C.
japonica using the chromosome image analysing
systemIV(CHIAS

IV;

http://www2.kobe-

u.ac.jp/~ohmido/index03.htm). The lengths and signal

positions of 5S rDNA were found similar in both C.
sinensis and C. japonica (Fig. 2).
Fig. 4 Anti-5 methyl cytosine antibody signal in theinterspecific hybrid cultivar Cha chukanbohon nou 1gou.

(A)

6. Conclusions
The quantitative analysis of a chromosome pairwith 5S rDNA indicated homology in thechromosomes of C. sinensis and C. japonica. In the(B)tea caffeine synthase gene, the two species
differed in the presence of an 83-bp in/del in thepartial sequence of the TSC1 intron. In theinterspecific hybrid, 5-methyl cytosine was
detected in one chromosome pair. These results
could be used to understand the progeny of crossFig. 2 DAPI density on the distance along the chromosome
with 5S rDNA signal. The vertical axis represents the grayvalue, and the horizontal axis represents the number ofpixels along the chromosome. The figures on the right side
show the ideograms of the chromosome with the 5S rDNA.
The green circle indicates the 5S rDNA loci. (A): C. sinensis(B): C. japonica

breeding between Camellia species and theirevolution.
Acknowledgements. I thank Sakurako Ogawa(National

InstituteofTechnology,

Numazu4. TCS1 partial sequencing

College Advanced Course), Shohei Sugiyama

A polymerase chain reaction (PCR) was performed(Graduate School of Agricultural Science, Kobe

using the primer pair 5′-tcttcaa aggcctgtcg tct-3′

University), and Fuuka Kitamura (Hiroshima

and 5′-tccccttgtttaatgccaag-3′, based on the TCS1

university) for their continuous efforts towards

mRNA sequence of tea (Accession AB031280). An

this work.

83-bp in/del sequence was detected in the clones(Fig. 2). After cloning, the sequence alignment

References

roughly divided the Camellia species into two

1.

groups—tea plant and other Camellia species (Fig.
3).

Moreover,severalpolymorphisms

2.

were

detected in the tea cultivar Sayamakaori (Fig.
2(B)).

5. 5-methyl cytosineIntheinterspecifichybridcultivarCha

chukanbohon nou 1gou, one chromosome
showedastrongFITC

signalupon

immunostaining (Fig. 4). No 5-methyl cytosine

27

Ackerman, W. L., Technical Bulletin, 1427, US
Government Print Office, 1971
Furukawa, K, Sugiyama, S., Ohta, T., Ohmido,N., Chromosome Science, 20, 9-15, 2017

Fig. 3 Partial sequence alignment of TCS1 cDNA in Camellia species. (A): C. japonica 1, 2, and C. sasanqua

28(B): C.

Life Science Database Integration and Its Application for
Medical Science
Susumu Goto1*
1* Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization ofInformation and Systems, 178-4-4 Wakashiba, Kashiwa, Chiba, 277-0871, Japan
Email: goto@dbcls.rois.ac.jp

Summary. Huge numbers of life science databases have been developed and are available as open data.
They can, therefore, contribute to the acceleration of open science, but they are quite heterogenous andtheir integrated use are still difficult. Database Center for Life Science (DBCLS) develops basic technologies
for their integration based on semantic web technology and provides a portal site for data represented in
the resource description framework (RDF) model, middleware for efficient access to the data, hackathons for
the RDF-based database developers and application programmers, and many other tools including
databases and web interfaces for medical science applications such as a genome variation database andphenotype-disease association tool for differential diagnosis.
Keywords. Resource Description Framework, SPARQL, genomic variations, diagnosis assistant, multi-omics.

1.

Introduction

This year, we present updates of the basic
technology

More than 1,700 databases have been developed indevelopments

in

DBCLS

and

applications of our resources and tools to medical

the life science research field and are available on

research.

the web [1]. They include data from various
research fields. In fact, the 2018 Nucleic Acids
Research database issue grouped the databases

2. Developments in DBCLS: Updates

into eight broad research categories, (i) nucleic acid

DBCLS develops basic technologies and application

sequence and structure, transcriptional regulation;

programs to integrate and maintain life science(ii) protein sequence and structure; (iii) metabolic

databases and to enhance their usability. The tools

and signaling pathways, enzymes and networks;

and activities of DBCLS are grouped into the(iv) genomics of viruses, bacteria, protozoa andfollowing four categories depending on the target

fungi; (v) genomics of human and model organisms

users.

plus comparative genomics; (vi) human genomic
variation, diseases and drugs; (vii) plants and (viii)

Application

other topics, such as proteomics databases.

programdevelopmentfor

researchers using web interface: Collaborating

Database Center for Life Science (DBCLS) [2] haswith DNA Data Bank of Japan (DDBJ) Center inbeen developing basic technologies for integrating

National Institute of Genetics, we are developing

those heterogeneous molecular biology databases

integrated search tools for genome and expression

by using the semantic web technology. Last year,

data without knowing detailed data structure to

we introduced the activities of DBCLS focusing on

support various levels of users for accessing data,

the development of middleware tools for accessing

because the query language SPARQL for accessing

andResource

RDF databases are difficult for initial users. These

Description Framework (RDF), and the coordination

utilizingdata

modelledbyare further applied for the integrated tools forof BioHackathon and SPARQLthon, forums for

medical research that are described in detail in the

database, ontology and program developers [3].

next section. Several RDF data can be searched via

29

naturallanguageprocessing

interfacecollected and organized so that each variation can

implemented in LODQA with an update for speech

be referenced with related links such as diseaserecognition.

information [5].

Efficient access and maintenance for the

PubCaseFinder. A differential diagnosis support

integrated database: To maximize the utilities ofsystem based on phenotypic similarity has been

RDF data, we have developed tools for accessing

developed for rare diseases by integrating disease-

SPARQLphenotype databases and text mining approach [6].

endpoints(RDF

databaseservers),

checking their status, building SPARQL queries, andsummarizing the query results. Currently, we are

4. Conclusions

applying a tool for retrieving distributed RDF data

As many life science data including omics data areto a protein sequence multiple alignment program.

open data and DBCLS has been focusing on suchdata for the integration using the semantic web

Basic technology development for databasetechnology, the databases and tools we provide areintegration: DBCLS supports database developers

suitable for open science. In addition, open-close

to convert their data into RDF formatted data, mostapproach would be necessary for the data such asof which are accessible via NBDC RDF portal [4] that

individual genome and personal data from cohort

is now consisting of ~47 billion triples from 21 data

studies.

sets of various types of omics and other data.
DBCLS also develops tools and dictionaries for

Acknowledgments. The author acknowledges all

knowledge extraction from texts in biomedical

the members of DBCLS and NBDC for theirliterature.

contributionindeveloping

ontologies

anddatabases,

tools,
andservices,coordinatingStandardization for database integration: We

hackathon and lecture series. The activity of DBCLS

work intra- and internationally with research

is funded, in part, by JST NBDC and ROIS

organizations that develop and maintain various

International Network Formation and Data Science

databases

Education programs.

and

have

contributed

to

the

development of international guidelines andprinciples for database integration using semantic

References

web technology. Hackathon series including

1. Rigden, D. J., Fernández, X. M., The 2018 Nucleic
Acids Research database issue and the online
molecular biology database collection. Nucleic
Acids Res., 46, D1–D7, 2018
2. DBCLS
homepage,
http://dbcls.rois.ac.jp/[accessed on: Oct 2018]
3. Goto, S., Life Science Database Integration
Based on Semantic Web Technology.
International Workshop on Sharing, Citation andPublication of Scientific Data across Disciplines,
21-22, 2017
4. NBDC RDF Portal, https://integbio.jp/rdf/[accessed on: Oct 2018]
5. TogoVar,
https://togovar.lifesciencedb.jp/[accessed on: Oct 2018]
6. Fujiwara, T., et al., PubCaseFinder: A CaseReport-Based, Phenotype-Driven DifferentialDiagnosis System for Rare Diseases. Am. J. Hum.
Genet., 103, 389-399, 2018

BioHackathon, SPARQLthon, RDF summit andBiomedical Linked Annotation Hackathon are
regularly held as forums of those collaboration.

3.

Application for Medical Science

In addition to the basic technology developments,
DBCLS has started application developments bycollaborating with National Bioscience Database
Center (NBDC) aiming at three main targets:medical science, useful substance production andbreeding research. As a first step, TogoVar and
PubCaseFinder have been developed for medical
science applications.
TogoVar. Genome sequence variations amongindividuals in the Japanese population have been30Perspective of Open Science in Japan driven by Integrated
Innovation Strategy
Kazuhiro Hayashi 1*
1* National Institute of Science and Technology Policy, Japan
Email: khayashi@nistep.go.jp

Summary. Open Science movement is transforming Science itself, and also transforming Industry andSociety, and “Science and Society”. Under this background, Open Science Policy has been developed andimplemented in Japan. Open Science is placed as a main function in “Integrated Innovation Strategy”
released on June 2018. In the strategy, Open Science is one of three pillars of “Source of Innovation” as wellas “Social Data” and “Official Administrative Data.” These are expected to contribute to Society 5.0 which isJapan’s vision for the future in the 5th Science and Technology Basic Plan. This new strategy aims to enhance
Data Science in new fields such as AI, Brain, and Research Data Management is getting more crucial not only
for Science but also for Industry and Society. Citizen Science and its expansion towards co-creative researchwould also emerge as a new trend of public engagement and new framework of society-related Science.
Keywords. Open Science, Integrated Innovation Strategy, CSTI, Data Science, Citizen Science.

1. Introduction

and Innovation (CSTI) on June 2018.

Open Science is one of important emerging

checking progress of the 5th Science andissuesaroundScience,Technology

AfterTechnology Basic Plan [5] and Comprehensive

andStrategy on Science, Technology and Innovation

Innovation Policy all over the world. In policy

2017 [6], IIS is for setting agendas with targets tocontext, Open Science is manly targeting to makeaccomplish them.

research outputs by publicly-funding as open as

During consideration ofdesigning IIS, various emerging topics werepossible and as closed as necessary to share, re-discussed at CSTI and Open Science was an issue

use them for innovation. [1] Open Science

which was not so emerging but promising issue

movement is now transforming Science itself

with long-term consideration.

beyond just changing the framework of scholarly

In the strategy, Open Science is one of threepublishing [2], and also transforming Industry andpillars of “Source of Innovation” (Knowledge

Society as well as “Science and Society”. [3] Under

Base) as well as “Social Data” and “Official

this background, Open Science Policy has been

Administrative Data.” These three are expecteddeveloped and implemented in Japan.

to contribute to develop Society 5.0 which isThis paper discusses perspective of Open

Japan’s vision for the future of super smart

Science in Japan with introduction of the latest

society in the 5th Science and Technology BasicSTI Policy called “Integrated Innovation Policy”
and its core module.

Plan.

2. Open Science as a Source of

Science policy is described to enhance researchInnovation

With concrete targets with figures, Openindata platform (data repository), making data

Integrated

policy of governmental research institutions,Innovation Strategy

advocacy and education, and developing a“Integrated Innovation Strategy” (IIS) [4] wassurvey for monitoring Open Science as well as

released by the Council for Science, Technology

31

promoting Data Management Plan by funding

to the web-native society (Society 5.0).

Eachagency and promoting Institutional Repository.

researcher, PI, and research institute will have toOpen Science is now recognized as a driver ofrecognize it and associate with it according to theinnovation to transform Science towards Society

affinity of their research to current movement,

5.0.

which would change their Science itself as OpenScience movement aims.

3. Perspective of Open Science andReferences

related activity

1.

This new strategy aims to enhance Data Sciencein emerging fields such as AI, Brain Science andMaterial Informatics. For example. the Cabinet

2.

Office started an expert meeting to discuss details
for AI. [7]For those new emerging field and even for theconventional

sciencefield,

Research

DataManagement (RDM) is getting more crucial. Since

3.

IIS is seeking multi-dimensional exploitation ofresearch data, RDM is not only for Science but
also for Industry and Society, implementing

4.

interoperable connection among them.
Open Science is also transforming “Science
Society.” RDM is effective to keep transparency of5.

Science itself. More proactively Citizen Science ischanging to exploit potential of Open Sciencevariously. Citizen is getting involved efficiently invarious research stage such as co-design and coproduction exploiting ICT. Some scientists earn
their research budget from citizen directly bycrowd-funding, not by indirect subsidies of public

6.

funding agency coming through as tax. It would
be towards co-creative research and it would also
emerging as a new trend of public engagement

7.

and new framework of society-related Science.

4. Conclusions
Open Science movement is affecting a wholesocial system to transform society itself to adapt

32

Open Science, European Commission,
https://ec.europa.eu/research/openscience/,
accessed on: Oct 2018
Hayashi, K., Revolution of process on
publishing and sharing towards OpenScience enhanced by openness of scholarly
communication. STI Horizon, 3, 35-39, 2017
(inJapanese),
http://doi.org/10.15108/stih.00092, accessed
on: Oct 2018
G7 SCIENCE MINISTERS’ COMMUNIQUÉ, 2017
http://www.g7italy.it/sites/default/files/docu
ments/G7%20Science%20Communiqu%C3%
A9.pdf , accessed on: Oct 2018
Integrated Innovation Strategy (in Japanese),
http://www8.cao.go.jp/cstp/tougosenryaku/,
accessed on: Oct 2018
A Promoting Open Science in Japan"Opening up a new era for the advancementof science" Executive Summary Report by theExpert Panel on Open Science, based on
Global Perspectives Cabinet Office,
Government of Japan, 2015,
http://www8.cao.go.jp/cstp/sonota/openscie
nce/150330_openscience_summary_en.pdf ,accessed on: Oct 2018
Comprehensive Strategy on Science ，Technology and Innovation for 2017,
http://www8.cao.go.jp/cstp/english/doc/201
7stistrategy_main.pdf, accessed on: Oct 2018
CSTI Expert Meeting on AI (in Japanese) ,https://www.kantei.go.jp/jp/singi/ai_senryaku/index.html, accessed on: Oct 2018

Current Situation of Data Archiving for Japanese Official
Statistics
Shinsuke Ito1*
1* Faculty of Economics, Chuo University, 742-1 Higashinakano, Hachioji-shi, Tokyo, 192-0393 Japan
Email: ssitoh@tamacc.chuo-u.ac.jp

Summary. In Japan, several types of official data are released under the Statistics Act including statistical
tables, original microdata, anonymized microdata, tailor-made tabulation and open data. Seven types ofAnonymized microdata from Japanese official statistics including the Population Census are publicly
released. Anonymized microdata from the 2000 and 2005 Census are currently available. Various disclosure
limitation methods including non-perturbative methods (sampling, recoding etc.) and perturbative methods
such as data swapping are applied to create anonymized census microdata. This paper describes themethods used for data archiving for official statistics in Japan, with a focus on both the current situation andfuture outlook.
Keywords. Data Archiving, Official microdata, Statistics Act.

1. Introduction

swapping is applied as an additional perturbative

In Japan, several types of official data are released

method to create Anonymized Census microdata.
In recent years, ‘Statistical Reform’ hasacross a variety of formats and based on data

attracted attention in Japan. Statistical reformconfidentiality and user needs. These data

focuses on improving the usability of official

include statistical tables, original microdata

statistical data as well as using big data and(individual data), anonymized microdata, tailor-

administrative data to foster evidence-based

made tabulation and open data. The data is

policy making by the Japanese national andreleased under the Statistics Act [1].

regional governments.

2. Archiving for Official Statistical

3. Conclusion

Data

The methods used for data archiving for official

In Europe and North America, several types ofstatistics in Japan are largely based on those

official microdata from the Population Census aredeveloped in Europe and North America. In orderpublicly available. In the United States, Public Use

to promote the secondary use of official statistics

Microdata Sample has been available since thein Japan, further research into data sharing

1960 Census [2]. In the United Kingdom, several

methods for official statistical data should betypes of Anonymized microdata such as Sample

pursued.

of Anonymised Records (SAR) are available.
At present, seven types of Anonymized
microdata

fromJapanese

official

References

statistics

1.

including the Population Census are publicly
released in Japan. Anonymized microdata fromthe 2000 and 2005 Census is also available.

2.

Various disclosure limitation methods such as
sampling, recoding, top (bottom) coding anddata deletion are applied prior to release. Data

33

Ito, S., Data sharing for official statistics:
Current situation and future outlook in Japan.
Journal of Information Processing andManagement, 58, 836-843, 2016 (in Japanese)
Zayatz, L., Disclosure Avoidance Practices andResearch at the U.S. Census Bureau: An
Update. Journal of Official Statistics, 23, 253265, 2007

Data management at Korea Polar Data Center,
Korea Polar Research InstituteDongchan Joo1*, Hyun-cheol Kim1, Goheung Kim1
1

* Korea Polar Data Center, Korea Polar Research Institute, Incheon, Korea South
Email: dc.joo@kopri.re.kr

Summary. The Korea Polar Data Center (KPDC) is in charge of collecting, managing, and sharing dataacquired through researches conducted by the Korea Polar Research Institute (KOPRI). The KPDC has large
volumes of research data of which most of them are shared for scientific and non-commercial purposes. In
recent years, data management has become increasingly important due to the surge of new data from more
active surveys than the past.
Keywords. Polar Data, Open Data, King Sejong Station, Jang Bogo Station, Dasan Station, IBRV ARON.

1. Introduction

and user-friendly system by applying GIS, data

The KOPRI opened its first Antarctic researchvisualization, and statistical techniques.

station, the Antarctic King Sejong Station, in 1988.
Subsequently, the KOPRI established the Arctic

2. PastDasan station in 2002 and IceBreaker Research

The KPDC is an organization dedicated for

Vessel ARAON in 2009. In 2011, the KOPRI

managing different types of data acquired during

established its second Antarctic research station,scientific researches that South Korea carries out

the Jang Bogo Station, located in the mainland of

in Antarctic and Arctic regions. South Korea, as an

the Antarctica.

Antarctic Treaty Consultative Party (ATCP) and an

Based on above infrastructures in Arctic andaccredited member of the Scientific Committee

Antarctic areas, researches on various fields suchon Antarctic Research (SCAR), established theas oceanography, biology, climate change,

center in 2003 as a part of its effort to joint

geophysics, remote sensing, and space weatherinternational Antarctic research.

have actively been carried out and have

In 2010, South Korea formulated a “Master-generated huge volumes of research data. In

plan for polar data center of South Korea” to build

2003, the KPDC was established to follow

its own polar data center. Since then, ongoing

demands for more systematic management ofefforts had been made to construct solid datadata, complying with the Antarctic Treaty Article

management infrastructures by implementing

III (1) (c) “Scientific Observations and results frompolar data management policies, developing andAntarctica shall be exchanged and made freely

operating a metadata management system, andavailable”, and to participate in such international

establishing the data management system forjoint effort for Antarctic research.

each research area and digital data management

The KPDC is designed to play as a hub of thesystem.

Korean polar research data from both Arctic andIn 2012, the KOPRI started providing training

Antarctic. All data acquired from polar researches

courses and raising awareness to researchers who

are registered at KPDC. Anyone can retrieve

are participating in polar studies in order increase

metadata and can request to use the original data

efficiency of data management. In addition, it

if necessary. Recently, a project to upgrade KPDC

established the schemes for data storage andsystem has started for better data management

back-up for acquired digital data to be safely
managed and permanently stored in its own

34system. Furthermore, it has contributed to polar

data by efficient management and distribution ofresearches of the international society by sharing

the data. It also aims to expand its infrastructure

thefor safe management and storage of polar data,internallyobtained

polar

data

and

experiences.

as well as to upgrade, on a continuous basis, theIn 2017, it reorganized the overall datasystematic

tools

for

the

comprehensive

sharing system to improve the accessibility andmanagement of polar data obtained from diverse

convenience for those who use the polar data,

research fields of polar regions. This new systemalong with the efforts for advancing the related

will be open in December, 2018 (Figure 1).

systems

supportedbythespecializedIT

4. Future

consulting.

Until now, the KPDC has focused its efforts on

3. Present

data collection and management. In the future,

The KPDC is developing a new system to provide

the KPDC is going to start a new challenge, bigdata science, which is beyond data management.

comprehensive and various polar information sothat researchers can have easy access to polar

Figure 1. Main screen of the KPDC web site under development (URL: https://kpdc.kopri.re.kr).

35

Activities of Polar Environment Data Science Center
Akira Kadokura1*
1* Polar Environment Data Science Center, DS, ROIS, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan
Email: kadokura@nipr.ac.jp

Summary. Activities of the Polar Environment Data Science Center (PEDSC) are introduced. PEDSC has been
established in the Joint Support-Center for Data Science Research (DS) of the Research Organization ofInformation and Systems (ROIS) in 2017. Purpose of the PEDSC is to promote collaboration with the dataobtained by the research activities in the polar region, and to play a key role of the data activity in polar
science to contribute to the global environment research.
Keywords. polar science, data science, open data, data publishing, data journal.

1. Introduction

at some specific locations as a sampled material,

Activity of the PEDSC is closely related with thee.g. rock, meteorite, ice core, sea water, air, etc.
For such sample data, both their catalogues andresearch and observation activities of theanalysis data are created and stored.

National Institute of Polar Research (NIPR). Main
purpose of the PEDSC is to promote the opening
and utilization of the various data stored in NIPR

3. Database system

in collaboration with universities and other

Each data mentioned in the Section 2 are stored

institutions in Japan and foreign countries. In

and archived in each database in each research

FY2017, we defined seven categories and made afield by each research group in NIPR in each form

yearly plan based on those categories to be

and style. There are also following generalcarried out during the whole 5 years of the DS

database systems for the data in NIPR, which canproject period until FY2021.

handle various data in various research fields.
3.1 Science database

2. DataScience database (https://scidbase.nipr.ac.jp/)
is a metadata database for all the data in all the

Data to be handled by the PEDSC are obtained inboth Antarctic and Arctic regions mainly by the

research fields of polar science, and has a close

four research groups in NIPR; Space and upper

relationship with international data activities, e.g.

atmospheric sciences group, Meteorology andNASA Global Change Master Directory (GCMD),

glaciology

group,

Geosciencegroup,

Standing

andCommitteeonAntarctic

DataManagement (SCADM) under the Scientific

Bioscience group. Various data in various research
fields have been obtained so far, e.g. aurora andCommittee on Antarctic Research (SCAR), etc.

upper atmosphere, meteorology and marine

3.2 Arctic Data archive System
Arcticscience, snow and ice, geology, geomorphology,Dataarchive

System(ADS)

seismology, gravity, and biology. Those data are(https://ads.nipr.ac.jp/) is a metadata and actual

stored and archived in various forms, anddata database system mainly for Arctic projects

basically classified in two categories, time series

such as GRENE (Green Network of Excellence) anddata and sample data. The former data are

ArCS (Arctic Challenge for Sustainability). ADS is

sampled at a fixed interval and recorded

also equipped with online visualization andcontinuously during some observation period.

analysis tools, and is used for collaboration withThe latter data are obtained at some specific date36

Japanese and international communities forpromote the data publication through the “Polar

Arctic research

Data Journal “, data journal of NIPR. (6) To

3.3 IUGONET systempromote collaboration with universities and otherinstitutions

IUGONET (Inter-university Upper atmosphere
GlobalObservation(http://www.iugonet.org/)

NETwork)systemis

metadata

a

in

Japan

and

international

communities. (7) To promote data science using
the database and database system.

database system which is developed in an inter-

Staff of the PEDSC in 2017-2018 JFY consists ofuniversity project among NIPR and 4 universities

a manager with one associate professor, three

for upper atmospheric science research. IUGONET

specially-appointed associate professors, and two

is also equipped with display and analysis

office assistants.

software tools (UDAS: iUgonet Data Analysis
Software) for actual data, and is close relationship6. Activities in FY2017-2018

with international data activities such as SPEDASIn FY2017, all the members of the PEDSC moved(SpaceAnalysisto the new building “Data Science building” from

Software) and SPASE (Space Physics Archivethe main NIPR building, and made a future

Physics

Environment

Data

Search and Extract) groups. Workshop and school

activity plan during FY2017-2021. As for eachfor the IUGONET system are regularly held for

category, (1) System design of the unified

students and researchers in Japan and abroad.

database system has been fixed in FY2018. (2)Several Antarctic data were processed by ADS.

4. Current problems

System design to develop ADS to AADS (Arctic &Current needs and problems on the data andAntarctic Data archive System) has been carried

database systems in NIPR are; (1) A synthetic

out. (3) Data management and publishing system

database system to cover, search and utilize all

for the PANSY data has been constructed. (4)the data and database in all the research fields isDatabase system for the Antarctic rock sample

not constructed and needed to understand thewas constructed. Some of the ice core data werepolar science activities as a whole. (2) Status ofregistered in the ADS system. (5) 2 data papers

the processing, archiving and opening of the data

were published and 5 papers are now underis in wide variety for each data and database,review for the Polar Data Journal. (6) ROIS-DS

depending on status of the resources ofInternational workshop on data science (IWDS)

manpower, hardware and software.

was held in FY2017 organized mainly by the
PEDSC, and the second IWDS will be held in
November, FY2018 (this time). (7) 3 and 7

5. Activity plan of PEDSC

proposals were accepted as PEDSC associatedCurrent targets of the PEDSC are defined in the

ROIS-DS collaboration program in FY2017 andfollowing seven categories; (1) To construct a

FY2018, respectively.

synthetic database for all the research fields ofpolar science. (2) To make the existing databasesystems (Science Database, ADS, IUGONET)7. Summary

upgraded and interoperable with each other. (3)PEDSC, established in FY2017, is now carrying out

To promote archiving, opening, and sharing ofvarious activities along a yearly activity plan

the time series digital data in each research field.

during FY2017-2021 in the seven categories(4) To promote archiving, opening, and sharing

specified to promote the archiving, opening andof the sample data in each research field. (5) To

sharing of the polar science data in Japan.

37

"Polar Data Journal": A new data publishing platform for polar
scienceAkira Kadokura 1*, Yasuyuki Minamiyama 2, Masaki Kanao 1, Takeshi Terui 2,
Hironori Yabuki 1, Kazutsuna Yamaji 3
1* Polar Environment Data Science Center, DS, ROIS, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan
2
National Institute of Polar Research, 10-3, Midoricho, Tachikawa, Tokyo 190-8518, Japan
3
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
Email: kadokura@nipr.ac.jp

Summary. Polar Data Journal (PDJ) is a free-access, peer-reviewed online journal of the National Institute ofPolar Research for publishing original research data/dataset, covering broad range of research disciplines
including Arctic, Antarctic, or other polar regions. PDJ was newly born in January, 2017, and the first data
paper of PDJ was published in October, 2017. So far, two papers were published, and currently five papers
are under review. A special issue is planned to invite more submissions to raise the value of the PDJ as thefirst data journal in polar science community.
Keywords. data journal, data publication, open data, polar science, DOI.

1. Aim and scope of Polar Data

theirguarantees

data

together with accepted manuscripts at the same

Polar Data Journal (PDJ) has been launched on 19

time.

January, 2017 by the National Institute of Polar
Research (NIPR) with a help of the JAIRO Cloud
repository

2. History of data publication of NIPR

systemSince(https://community.repo.nii.ac.jp/) of the National

had

been

“JARE Data Reports” is a journal for annual reportfurthering the reuse of high-quality data and theof the scientific data obtained in each Japanese

benefit to polar sciences. PDJ aims to cover broad

Antarctic Research Expedition (JARE), and has

range of research disciplines involving Arctic,

been launched on August, 1968. In December,

Antarctic, or other polar regions, especially earth

1996, “NIPR Arctic Data Reports” has beenand life sciences. PDJ primarily publishes data

launched as a data journal for the scientificpapers, which provide detailed descriptions of
Methods,

Syowa

journal for recording the Antarctic activities.

for publishing original research data/dataset,(e.g.

stationhas been launched in December, 1957 as apeer-reviewed and online journal. It is dedicateddata/dataset

Antarctic

established in January, 1957, “Antarctic Record”

Institute of Informatics (NII). PDJ is a free-access,research

PDJ

authenticity by publishing all review reports

Journalonlinemanuscripts.

activity in Arctic region.

Data

Consideration oflaunching the PDJ started in June, 2015. Many

Records, Technical validation). PDJ does notaspects of the PDJ has been considered (e.g.

require any new scientific findings, so PDJ also

Submission guideline, selection of platform,

welcomes submissions describing past valuable

development

data/dataset which has not been published yet.

environment,

policy,review

process, journal name, board organization, etc.)

PDJ requires submitted papers to be passed our

before launching on January 19, 2017. In future,peer-review process. PDJ also requires thatboth “JARE DATA Reports” and “NIPR Arctic Dataauthors should deposit their data/dataset toReports” will be combined and moved to PDJ.

trustworthy data repository before submitting38

3. Board organization of PDJ

persistent identifier (such as DOI); 2. The

Editorial board of PDJ consists of 12 board

data/dataset in the repository must be available
free of charge and without any barriers except formembers, including two foreign members, threea standard registration to get a login free of

non-NIPR domestic members, and seven NIPR

charge; 3. Anyone must be free to copy, distribute,members. Advisory board of PDJ consists of 10
board

members,

includingeighttransmit, and adapt the datasets as long as they

foreignmembers and two non-NIPR domestic members.

give credit to the original authors.

4. Review process of PDJ

6. Submission guidelines

Review process of PDJ is as follows: 1. Authors

Structure of the data paper submitted to PDJ
should consist of the following items: title,

submit their manuscript by using an online

authors, affiliations, abstract, background andEditorial Manager (EM) system, and they also

summary,

upload their original data to an appropriate

location(orobservation),

methods, .data records, technical validation,

repository; 2. EM sends a receive information to

usage

editorial office (Office); 3. Office confirms thenotes(optional),

acknowledgements,

author contributions, competing interests, figures

original data on the repository; 4. Office copies(optional), tables, references, data citations.

the original data to the JAIRO Cloud; 5. EM sends
a receive information to editorial board (Editor); 6.
Editor confirms the original data; 7. Editor invites

7. Current and future of publication

two referees (Referee); 8. Referee receives the

So far, 8 submissions were received. 2 weremanuscript; 9. Referee reviews both manuscript

published, 1 is on the editor’s decision afterand original data; 10. Referee sends reply

reviewing, 4 are under review, and 1 was rejected.

comment to EM; 11. EM sends the reply comment

To enhance and invite more submission, a special

to Editor; 12. Editor sends feedback to EM; 13.

issue is now planned. Recruit and support

Authors receives the feedback from EM; 14. After

activities for inviting submissions are also

accepted, Office copies the original data atnecessary for the data obtained in the JARE, arctic

accepted timing to the JAIRO Cloud; 15. Officeprojects, and in the international programs ofrequests the manager of the data repository to

SCARpublish a Data DOI to the accepted data; 16. EM(ScientificCommitteeonAntarctic

Research) or IASC (International Arctic Science

sends a proofreading comment to Office; 17.

Committee), etc.

Office sends publish report of all review
processes; 18. EM publish manuscript with data

8. Summary

DOI using the JAIRO Cloud.

PDJ is a new data publishing platform for polar
science, born in January, 2017. It is important to

5. Appropriate repository for PDJ

invite more submissions of data papers to raise

As the appropriate repository for uploading thethe value of the PDJ as the first data journal in thedata for PDJ, following criteria are required: 1. The

polar

data/dataset in the repository must have a39sciencecommunity.

Education Programs for AI/IoT/BigData Skills Development
at the Medical and Drug Discovery Data Science Consortium
Eli Kaminuma1*, Hiroshi Tanaka 2
1* Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo, Tokyo, 113-8510, Japan
Email: ekmds@tmd.ac.jp

Summary. The number of healthcare data science studies is increasing nowadays, however there are few
data scientists familiar to life science domain in Japan. To solve this problem, we established the medical anddrug discovery data science consortium in 2017, for assisting consortium participants to increase their
AI/IoT/BigData skills in healthcare domain. The mission of the consortium is (1) enhancing education
programs for doctoral students and industrial careers and (2) conducting open innovation meetings to
exchange advanced progress of healthcare data science.
Keywords. Data Science Consortium, Medical Care, Drug Discovery, AI/IoT/BigData, Open Innovation.

1. Introduction

Science consortium, which is characterized as

In healthcare domain, education programs tofollows.

develop AI/IoT/BigData skills at training courses

 One

are not popular in Japan. Thus, human resources

of

the

2017-18

D-DRIVE

grantwith novel data science skills such as artificial

programs

intelligence and big data are few in Japanese

In 2017 D-DRIVE grants, the Japanese

healthcare research community. To resolve thisgovenment MEXT selected four institutes ofproblem, we established the Medical & Drug

TMDU, University of Electro-Communications,discovery Data Science (MD-DS) Consortium [1].

Waseda University, and Osaka University.

The consortium aims to develop education

Then Hokkaido University was selected forprograms to acquire the ability to combine

2018 D-DRIVE program.


knowledge of medical and drug discovery with

Characteristicfeatures

of

MD-DS

consortium among D-DRIVE members

novel data science skills. In this presentation, oureducation programs for healthcare data scienceOf five data science consortiums, our MD-DS

are introduced.

consortium represents characteristic featureof healthcare domain such as medical careand drug discovery.

2. Medical & Drug Discovery Data

The data science

consortium of Hokkaido university is also an

Science Consortium

organization concerning to infrastructure

In 2017, the Japanese government MEXT started

domain.

data science education grants named by Doctoral
program for Data-Related InnoVation Expert (D-The D-DRIVE grant program seems to refer to the

DRIVE) [2]. The D-DRIVE program focuses on

INSIGHT Data Science Fellows Program [3] of US

AI/IoT/BigData skills development for the fourth

government

industrial revolution. Our Tokyo Medical andtobridgethegapacademia and a career in data science.

Dental University (TMDU) was adopted as a DDRIVE grant program of the first call. Thus we

3. Education Programs forestablished the Medical & Drug discovery Data40

betweenAI/IoT/BD Skills Development at
4. Conclusions

the MD-DS Consortium

We introduced the outline of the Medical & Drug

In 2018, the MD-DS consortium called respective

discovery Data Science consortium of a Japanese

30 participants of doctoral students and industrial
careers. As the result, fifty-three participants weredata-related educational grant program. The MD-

recruited

DS consortium holds open innovation meetings

from

22

companies

and8four times a year in addition to developing

universities/institutes.

human resource education programs. Then,

The curriculum of the MD-DS consortiumpromoting open innovation is also a valuable

provides two main training components oftarget of the MD-DS consortium.

“Medical Big Data” and “AI Drug Discovery”.
Moreover,basic

training

components

areconstituted by “Data Science Basics”, “Genomic

Acknowledgments. We are thankful to Takeshi

Science”, “Computational Programming”, “Life

Hase, Katsuyuki Takeuchi, Yuko Igarashi, Shuhei

Science Ethics”. The total number of consortium

Mitome, Akiko Miyamoto, Akihiro Sekiguchi,classes is around 100. Then programming

Hiromi Shimoda as staffs of the medical and drug

practices supported statistical analysis anddiscovery data science consortium. This study

machine learning with R and Python languages.

waspartially

supportedbyaJapanesegovernment MEXT grant of the Doctral program

Most classes are provided between July tofor Data-Related InnoVation Expert.

December, and then visiting training programs
are started at October. Visiting training practicesof data science are formatted to internship for

References

doctoral students and programming practices at

1.

major research institutes for industrial careers.
Table 1 presents the list of research institutes for2.

visiting training programs.
All classes and visiting training programs are3.

reviewed by questionnaire survey to participants
and

thus

the

surveyresults

contribute

improvement of the quality of education

Medical & Drug discovery Data Science
Consortium, http://md-dsc.com/ [accessed
on: Oct 2018]
Japanese government MEXT D-DRIVE grant,
http://www.mext.go.jp/a_menu/jinzai/data/i
ndex.htm [accessed: Oct 2018]
INSIGHT data science fellows program,
https://www.insightdatascience.com/[accessed: Oct 2018]

programs.
Table 1. List of research institutes to provide visiting training programs for industrial careers.
Research Institutes
National Center for Global Health and Medicine,National Center for Neurology and Psychiatry,
Japanese Foundation for Cancer Research, TMDU

Training Programs
EMR/EHR Data Mining, Cancer Image Analysis,
Deep Learning

Institute Location
Tokyo

Tohoku Medical Megabank Organization

Precision Medicine

Miyagi

Keio University

Metabolomics

Yamagata

41

A decade of history for polar data management in Japan
Masaki Kanao1*
1* Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 10-3,
Midori-cho, Tachikawa-shi, Tokyo 190-8518, Japan
Email: kanao@nipr.ac.jp

Summary. Diverse data accumulated by many science disciplines make up the most significant legacy of theInternational Polar Year (IPY2007-2008). The Polar Data Center (PDC) of the National Institute of Polar
Research (NIPR), followed by the Polar Environment Data Science Center (PEDSC) of the Joint Support-Center
for Data Science Research (DS) have responsibility to manage these polar data in Japan as a National
Antarctic Data Center (NADC). During the IPY, a significant number of multi-disciplinary metadata records
were compiled from IPY- endorsed projects. A tight collaboration has been established between the Global
Change Master Directory (GCMD), the Polar Information Commons (PIC), and the newly established World
Data System (WDS). In this presentation, a decade of history of polar data management is demonstrated.
Linkages of data sharing among Asian Forum for Polar Sciences (AFoPS) countries, moreover, should bepromoted by the involved countries in near future.
Keywords. International Polar Year, data management, Polar Environmental Data Science Center, National
Antarctic Data Center, Asian Forum for Polar Sciences.

1. Introduction

available promptly by Internet technologies such

At the 22nd Antarctic Treaty Consultative

a repository network service. In addition toactivities in polar science communities of SCAR

Meeting (ATCM) in 1998, affiliate countries were

and the International Arctic Science Committee

obliged to ensure that scientific data collected
from

Antarctic

programs

could

be

(IASC),

freelytighterlinkages

expected

tobeestablished with other cross-cutting scienceexchanged and used. Following Article No.III.1.cbodies under ICSU, such as CODATA, and WDS.

of the Antarctic Treaty, each country is required

Linkages among these data-management bodies

to establish NADC and to properly disclose theneed to be strengthened in the post IPY era.

data collected from involved scientists. The
PDC/PEDSC has performed the function of a
NADC for Japan and established a data policy in

2. Data Management

February 2007, based on the requirements of the

The International Polar Year (IPY 2007-2008) was

Standing

Data

the world’s most diverse science program. It was

Scientificconducted during the 50th anniversary of theCommittee on Antarctic Research (SCAR). This

International Geophysical Year (IGY 1957-1958).

contributed to the subsequent SCAR Data and

The IPY greatly enhanced the exchange of ideas

Information Management Strategy [1].

across nations and scientific disciplines to unveil

Committee

Management

Dedicated(SCADM)

dataon

Antarcticofservices

the

have

been

the status and changes of planet Earth as viewed

conducted by PDC/PEDSC as a member of NADC

from polar region. The interdisciplinary exchange

under SCAR. Several different aspects of scientifichelped us understand and addressed granddata collected in polar region have greatchallenges such as rapid environmental change

significance for global environmental research. To

and its impact on society. Eventually, Japaneseconstruct an effective framework for long-term

researchers participated to 63 projects endorsed

strategy of the polar data, data must be madeby the IPY Joint Committee. The huge amount of42

data accumulating during IPY should be the mostservice was coupled with a cloud-based
repository that may not have a suitable archive
elsewhere. NIPR/DS have made contributions to
PIC, both by attaching badges and registration in
the repository. As of October 2017, Japan had
contributed more than 50 data sets to the PIC.
Polar data have great relevance for modern,global environmental research well beyond thepolar region. It is critical to explore the cloudapproaches such as the PIC to develop an
effective framework for open and long-term
stewardship of polar data. The status of datamanagement before and after the IPY in Japanwas introduced in this report. Several different
aspects of the scientific data collected in the polar
region have great advantage for global
environmental research as well as in future.
Linkages of data sharing among Asian Forum on
Polar Science (AFoPS) countries, moreover,should be promoted by the involved countries innear future.

important legacy if it is well preserved andutilized [2].
The

science

databaseprovidedby

PDC/PEDSC has a tight connection with AMD in
GCMD. In addition to the IPY-related data, datafrom Japanese national and other international
projects had been compiled. In totally, 300
metadata were compiled in Japanese Antarctic
portal in GCMD. PDC/PEDSC stores its metadata
in their original format, but this includes the main
items listed in GCMD Directory Interchange
Format (DIF). There are tight cross-linkages incorresponding metadata in AMD. Metadata
collected by IPY projects for Japan have also been
accumulated in an IPY portal of GCMD. More than
250 metadata from Japan were stored in the IPY
portal [3, 4]. This constitutes a significant
proportion of all IPY metadata to GCMD.

3. Data Legacy

Acknowledgments. The authors express their

SCADM has been strongly connected with IPY
data-management activities (IPY Data andInformation Service: IPY-DIS). IPY data policyemphasized a need to make data available on the“shortest feasible timescale.” In accordance withIPY data policy, IPY-DIS recommended that data
be formally cited when used, and the IPY Data
Committee has developed initial guidelines for
how data should be cited. The guidelines
harmonized different approaches, and they
adopted by many data centers relating polar area.
After the end of IPY, a new initiative, the Polar
Information Commons (PIC), began as aframework for open and long-term stewardshipof polar data and information [2]. The PIC serves
as an open, virtual repository for vital scientific
data and information and provides a shared,
community-based cyber-infrastructure fostering
innovation
andimprovedscientificunderstanding while encouraging participation in
research, education, planning, and managementin polar region. PIC developed specialized toolsthat produce a small, machine-readable “badge”
that is attached to the data. However, the badge
requested data users to adhere to basic ethicalnorms of data use including proper citation. Thisappreciation to all collaborators of the IPY
activities. They also acknowledge the members of
SCADM and IPY Data committee for their efforts
to adhere to data-management issues.

References
1.
2.

3.

4.

43

Finney, K., SCAR Data and Information
Management Strategy 2009-2013. SCAR Adhoc Group on Data Management, 34, 2009
Parsons, M. A., Godoy, Ø., LeDrew, E., de Bruin,T., Danis, B., Tomlinson, S., Carlson, D., A
Conceptual Framework for Managing Very
Diverse Data for Complex, Interdisciplinary
Science. Journal of Information Science, 1-21,
2011
Kanao, M., Kadokura, A., Okada, M.,
Yamnouchi, T., Shiraishi, K., Sato, N., Parsons,M. A., THE STATE OF IPY DATAMANAGEMENT:
THE
JAPANESECONTRIBUTION AND LEGACY. Data Science
Journal, 12, WDS124-WDS128, 2013
Kanao, M., Okada, M., Friddell, J. andKadokura, A., Science Metadata Management,Interoperability and Data Citations of the
National Institute of Polar Research, Japan.
Data Science Journal, 17, 1–6, 2018

Data and metadata sharing among AFoPS countries
Masaki Kanao1*
1* Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 10-3,
Midori-cho, Tachikawa-shi, Tokyo 190-8518, Japan
Email: kanao@nipr.ac.jp

Summary. The Polar Environmental Data Science Center (PEDSC) of the Joint Support-Center for Data
Science Research (DS), the Research Organization of Information and Systems (ROIS) has a responsibility to
manage the data for Japan as a National Antarctic Data Center (NADC) during the last few decades. At theInternational Polar Year (IPY2007-2008), a significant number of multi-disciplinary metadata/data have been
compiled mainly from IPY- endorsed projects. These collected metadata have a tight collaboration with the
Global Change Master Directory (GCMD), the Polar Information Commons (PIC), as well as several centersbelonging to the World Data System (WDS). Among activities in polar communities of the ScientificCommittee on Antarctic Research (SCAR) and the International Arctic Science Committee (IASC), tighter
linkages of data sharing within the Asian Forum for Polar Sciences (AFoPS) countries should be promoted by
the involved Asian countries.
Keywords. Polar Environmental Data Science Center, data sharing , polar communities, AFoPS.

1. Introduction

Data and Information Management Strategy.
Several different aspects of scientific datacollected in the polar region have greatsignificance for global environmental research inthis century. To construct an effective framework
for long-term strategy of the polar data, data
must be made available promptly and newInternet technologies such a repository network
service must be employed. In addition to theactivities in polar science communities of SCAR
and the International Arctic Science Committee(IASC), tighter linkages must be established withother cross-cutting science bodies under ICSU,
such as CODATA, and WDS.

Diverse data accumulated by many science
disciplines make up the most significant legacy of
the International Polar Year (IPY2007-2008). The
Polar Data Center (PDC) of the National Instituteof Polar Research (NIPR), followed by the Polar
Environment Data Science Center (PEDSC) of the
Joint Support-Center for Data Science Research(DS) have responsibility to manage these polar
data in Japan as a National Antarctic Data Center(NADC). During the IPY, for instance, a significant
number of multidisciplinary metadata records
were compiled from IPY- endorsed projects. A
tight collaboration has been established between
the Global Change Master Directory (GCMD), thePolar Information Commons (PIC), and newly
established World Data System (WDS). In thispresentation, a history of data management at
NIPR is summarized, focusing on the era after the
IPY.
The PDC/PEDSC has been performed thefunction of a NADC for Japan and established adata policy in February 2007, based on therequirements of the Standing Committee on
Antarctic Data Management (SCADM) of the
Scientific Committee on Antarctic Research(SCAR). This contributed to the subsequent SCAR

2. AFoPS and perspective
The Asian Forum for Polar Sciences (AFoPS) is a
non-governmental organization established in
2004 to encourage and facilitate cooperation for
the advance of polar sciences among countries
in the Asian region. The Forum consists of its six
members, i.e, the national polar research
institutions representing China, Japan, South
Korea, India, Malaysia and Thailand. AFoPS also
has four observer countries: Indonesia,
Philippines, Sri Lanka and Vietnam, respectively.
The objectives of AFoPS has been recognized as44

the value of scientific research in bi-polar regions
for the benefit of human activities, recognizing
the importance of international cooperation in
the polar regions and the need to work closelywith other national operators, as well as aiming toserve the common interests in both polar
sciences and logistics. Member countries will
work together for the tasks as follows; provide afoundation for cooperative research activities;
present Asian achievements toward internationalpolar communities;
encourage more Asian
countries’ involvements in polar sciences.
Major Activities of AFoPS include; provide aforum to seek a common view on polar affairs
among member countries; develop and support
cooperative programs on polar activities (i.e., joint
science projects, logistic cooperation andpersonnel exchange program between polar
expeditions and institutes, etc.); convene joint
symposia and workshops for sharing scientificresults, information and experience joint
symposium; conference activities within AFoPS
working groups (WGs); support non member

countries to develop their national polar
programs; invite scientists to field expeditions
and institutes; invite scientists to AFoPS meetings;
provide personnel training and cooperation inoutreach activities; produce joint publications onpolar sciences.
Among data activities in polar communities of
SCAR and the International Arctic Science
Committee (IASC), moreover, tighter linkages ofdata sharing among AFoPS countries should bepromoted in near future by the involved
members of Asian countries. Establishing a new
portal inside GCMD, for example, the simple andfirst step for data sharing and inter-operability.
Detail discussion will be expected in thisconference of DSWS-2018.
Acknowledgments. The authors would like toexpress their appreciation to many collaborators
involving polar data communities, in particular to
the member of PEDSC, data committee members
on SCAR, IASC, WDS, CODATA and IPY.

45

Development of Japanese Research Data Discovery Service
Fumihiro Kato1*, Teruhito Kanazawa1, Kei Kurakawa1, Ikki Ohmukai1
1*National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda, Tokyo, 101-8430, Japan
Email: fumi@nii.ac.jp

Summary. Finding and reusing research data are a crucial part of Open Science. CiNii Research is a discovery
service for finding scholarly resources including research publications and datasets produced by research
projects in Japan. It aggregates a variety of metadata related to Japanese research activities. Sources of
metadata contain NII scholarly information services, institutional repositories, persistent identifiers and other
scholarly services. It extracts research entities from aggregated metadata to construct a large-scale scholarly
knowledge graph that illustrates relationships among research entities. It helps researchers not only to findresearch datasets and relevant articles used for their daily research activities but also to get an idea of a
research theme to start their project. This presentation introduces development progress of CiNii Research.
Keywords. Open Science, Research Data, Discovery Service, Knowledge Graph.

1. Introduction

2. Aggregation

National Institute of Informatics (NII) hosts

The first component is to aggregate metadata ofscholarly information services for researchers.

research entities related to research projects in

1

CiNii is a discovery service by NII for JapaneseJapan. NII has already collaborated with Japanese

research literatures such as articles, books, anduniversities and institutions to collect metadata

dissertations. It harvests and integrates metadatabehind NII scholarly information services. IRDB 2 isof publications from institutional repositories, thea national aggregator of institutional repositories.

National Diet Library, academic societies and

It includes about 2.9 million records from 685

other scholarly databases in Japan. As sharing,

repositories as of Sep. 2018. And the number offinding and reusing research data are one of key

datasets is about 55 thousand records (2.5% ofconcepts of Open Science, NII has launched atotal). As CiNii uses metadata collected by IRDB,CiNii Research project to enhance CiNii to support

we will update IRDB with JPCOAR Schema 1.0 3

research data as a first-class citizen since 2017.

which is the latest metadata schema for Japanese

CiNii Research aims to enable search andinstitutional repositories to support new features

discovery of publications and datasets produced

like identifiers and open access policies.

by research projects in Japan. CiNii Research

Another aggregator is KAKEN 4 that collectsconsists of three components. The first one is toand hosts result reports of Grants-in-Aid for

aggregate metadata related to research projects

Scientific Research (KAKENHI) which is one of thein Japan. The second one is to extract researchmajor research funds by the Government ofentities and their relationships from aggregatedJapan. NII begins to collect persistent identifiers

metadata to construct a large-scale scholarly

like DOI. Japan Link Center (JaLC) 5 is the DOI

knowledge graph. The last one is to provide aregistry agency in Japan and NII is one of board

discovery service for research entities by indexing

members of JaLC. Hence JaLC will be our primarynodes of the knowledge graph. This presentation
reports on the progress of the development.

2

http://irdb.nii.ac.jp/analysis/index_e.php
https://schema.irdb.nii.ac.jp/en4
https://kaken.nii.ac.jp/en/5
https://japanlinkcenter.org/top/english.html
31

https://ci.nii.ac.jp/en46

universities and librarians to input relevant

DOI source as Japanese repositories use JaLC to
assign DOIs to research entities including

identifiers in their public repositories. They will

research datasets.

help us to grow and refine our knowledge graph.
Knowledge graph is also important for a
global collaboration with other discovery services.
Scholix [1] provides an interoperability framework
for exchanging links between scholarly literaturesand datasets. OpenAIRE provides LOD [2] to share
their links. Our links should be also shared withinternational activities in future.

4. Discovery Service
CiNii Research provides a simple input form to

Figure 1. Entity Types

search by keywords. A user can select a typedescribed in the section 3 from tabs before

3. Knowledge Graphsearching words. If a user selects the “dataset” tab,

Constructing a knowledge graph of researchsearch results are filtered only for datasets. CiNii

entities is an essential part of a modern discovery

Research does not support a facet search that

service as links between research entities help totypical discovery services implement for theirfind further related research entities. Figure 1

search results to keep the results as much as

describes targeted entity types including Product,

simple currently.

Researcher, Project, Organization and Fund.

The discovery service will connect to our

Product is defined as a superset of Article, Book,

research data management platform called

Dissertation and Dataset. CiNii Research currently

GakuNin RDM [3] to import specific research data

focuses on Product, Researcher and Project toafter finding it from search results.

extract research entities from metadata andidentify them with persistent identifiers and5. Conclusions

name disambiguation techniques.

CiNii Research is a discovery service for JapaneseAcquiring links between identified entities isscholarly resources based on knowledge graphthe hardest part of creating a knowledge graph as

currently under development. It will help

explicit links in metadata are rather a few. Our

researchers to find research datasets and otherchallenge is to extract links from scholarly

resources related to research activities in Japan.

services to integrate into the knowledge graph.
For instance, KAKEN has reports, products andresearchers of projects so that the system canReferences

obtain links among them. The main issue of this1.

challenge is that each scholarly service isindependent and only a part of national
researcher identifiers is shared. Therefore, linking2.

the same entities among services is important.
Metadata including identifiers of research
entities and links between research entities will
increase in future as NII has been developing a

3.

new version of institutional repository systemcalled WEKO3 to implement the JPCOAR Schema.
The current WEKO is used by about 500 Japanese

47

Burton, A., Koers, H., Manghi, P., et al., The
Scholix Framework for Interoperability in
Data-Literature Information Exchange. D-Lib,
23, 1, 2017
Alexiou, G., Vahdatai, S., Lange, C.,
Papastefanatos, G., Lohmann, S., OpenAIRE
LOD Services: Scholarly Communication Data
as Linked Data. Save-SD 2016, Lecture Notes on
Computer Science, 9792, 45-50, 2016
Komiyama, Y., Yamaji, K., Nationwide
Research Data Management service of Japanin the Open Science Era. In: 6th International
Congress on Advanced Applied Informatics,
Hamamatsu, Japan, 129-133, 2017

Exploring public attitudes toward scientific research withvisitor surveys and nationally representative surveys
Naoko Kato-Nitta1,2*, Tadahiko Maeda1,2
1* Center for Social Data Structuring, Joint Support-Center for Data Science Research, Research Organizationof Information and Systems (ROIS), 10-3 Midori-cho, Tachikawa, 190-8562, Japan2
Department of Statistical Data Science, The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa,
190-8562, Japan
Email: naokokn@ism.ac.jp
Summary. Despite the promotion of public engagement in science, there has been little empirical researchon the attitudinal characteristics toward scientific research of visitors in science events and the extent towhich such individuals are distinctive from the general population. We statistically explored this topic bycontrasting samples from visitor surveys and archived nationally representative surveys. The results showedthat the visitors believed more in the value of scientific research than the general public, but there was nodifference regarding assessment of the levels of national science or of the national economy. Furthermore,
visitors were more actively engaged than the general public not only in scientific activities such as going toscience museums and attending science cafes, but also in literary and artistic activities such as going to art
museums and reading novels or history books. This research should be a good practice for utilizing archived
social survey data for promoting public communication of science.
Keywords. science communication, archived social survey data, visitor survey, the Japanese National
Character Survey, scientific outreach activities.

1. Introduction
Scientificresearch

bolsters

national

distinctivenessofvisitors

communication

activitiesinbysciencestatisticallycontrasting them to the respondents of nationallycompetitiveness and economies and often results

representative surveys [2]. Further, we exploredin industrial innovations that provide solutions todeterminants

the environmental challenges such as climateofvisitors’

exhibitviewingbehaviours by utilizing different methods ofchange. Yet, not all scientists know the bettermeasurement.

ways to convey their scientific findings to the lay
public. In such circumstances, the governments inmany countries including Japan promote the2. Methods

scientific contributions of scientists to the public

The statistical analyses used data from thethrough social activities such as the national

following three surveys:

institutes'

openhousesbydisseminating

Survey 1: a 2012 visitor survey at the Institutescientific information to visitors [1].

for Molecular Science (IMS),

Better understanding their visitors to such

Survey 2: thescientific outreach events and how ‘the lay public’

Character Survey,

reacts for their scientific findings should be one ofSurvey 3: a 2014 web-based Internet survey ofthe great interests for scientists and science

Japanese citizens.

communicators; however, there is little data

Statistical comparisons utilizing the above

showing how visitors to scientific outreach events

data were made based on the following

are distinct from the general public. We

hypotheses:

elucidated

socio-culturalandattitudinal

48

2013

Japanese

National

H1: Visitors (to scientific outreach activities)

Scientific experts tend to think the people withparticipate in science-related activities morewhom they engage in dialogue during scientificoften than the general public.

outreach activities are the 'general public.

H2: Visitors participate artistic and literary-

However, our findings suggest that there are notrelated activities more often than the public.

only socio-cultural and attitudinal differences

H3: Visitors show more favourable attitudes

between visitors to their exhibits and the general

toward the value of science than the public.

public but also behavioural differences between

H4: Visitors’ assessments of the level ofhighly engaged visitors and other visitors.

Japanese science, art, or economy are not

The results provide essential information fordifferent from those of the public.

scientists

involved

intheinstitutional

communication of science by providing insight
The above comparisons include analyses of

into potential or non-attending visitors. We

estimates adjusted for the distributions of thewould like to emphasize that the empiricalattribute variables of age, gender, and educationcomparison between the general public and thewith direct method of standardization.

visitors of the scientific institution was madeFor the above survey 1, we further assessed

possible with the archived social survey data that

visitors’ exhibit viewing behaviours of the total

are representative of Japanese population.

viewing time and the total number of exhibits
viewed, and explored the determinants of thoseAcknowledgments. This study was conductedbehaviours with regression analysis.

under the ISM Cooperative Research Program(2016 ISM.CRP2- 2031, 2015 ISM.CRP 2-2039) and3. Results

KAKENHI (15H03424). The data for Survey 3 used

The visitors believed more in the value ofthe data developed for KAKENHI (24380118).

scientific research than the general public, butthere was no difference regarding assessment ofReferences

the levels of national science (Table 1) or of the1.

national economy. To sum up the statistical tests,
all

the

above

hypotheses

werebasically

supported.

2.

The results of regression analyses showed thatpeople who have higher scientific cultural capital
viewed more exhibits and stayed longer at theevents.

Kato-Nitta, N., The influence of cultural
capital on consumption of scientific culture.
Public Understanding of Science, 22.3, 321-334,
2013
Kato-Nitta, N., Maeda, T., Iwahashi, K.,
Tachikawa, M., Understanding the public, thevisitors, and the participants in science
communication
activities.
PublicUnderstanding of Science, published online
beforeprint,
doi.org/10.1177/0963662517723258, 2017

4. Conclusions
Table 1. Differences in attitudes toward science

Items/Categories

Survey 2(n=1585/1572)%
SE
Item 1.Science improves daily life?
1.A lot
38.9% 1.224%
2.A little bit
45.7% 1.251%
3.Not at all
10.4% 0.767%
4.Other/Don't know
5.0% 0.547%
Item 2. Level of S & T in Japan
1.Very high34.7% 1.201%
2.Fairly high
52.2% 1.260%
3.Low(fairly/very)
7.3% 0.657%
4.Other/Don't know
5.7% 0.586%

Survey1
Crude (n=299 to 302)
Diff test's Z*4

%

SE

70.9%
23.6%
1.0%
4.5%

2.658%
2.486%
0.590%
1.207%

10.942-7.945-9.695-0.402

35.7%
49.1%
9.6%
5.5%

2.809%
2.931%
1.729%
1.336%

0.329-0.9671.247-0.15649

Adjusted(n=261 to 292)
Diff test's Z*4%

SE277.002(df=3)
<.001

61.9%
31.2%
1.3%
5.6%

4.402%
4.318%
1.027%
2.225%

5.037-3.229-7.0730.248

85.883(df=3)<.001

2.624(df=3)
0.453

38.5%
46.1%
9.6%
5.7%

4.528%
4.550%
2.650%
2.332%

0.810-1.299
0.853
0.003

3.070
(df=3)
0.381

Chi-squared Test

Chi-squared Test

AI and Data Science - Machine Learning as Digital Catalyst for
Data Curation
Asanobu Kitamoto 1*, 2*
1* Center for Open Data in the Humanities (CODH), Joint Support-Center for Data Science Research, Research
Organization of Information and Systems (ROIS)
2* National Institute of Informatics, 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
Email: kitamoto@nii.ac.jp

Summary. Data is a valuable asset, but how can we characterize its value? We propose four types of values,
namely intrinsic value, basic value, added value and persistent value, and discuss how machine learning cancontribute to increasing basic and added values. In the examples of automatic tagging for image collection,
we realized that the role of machine learning-based approach is to give tags of general nouns, and provides
a good starting point for human-based higher quality tags of proper nouns. We finally propose the “digital
catalyst” metaphor to emphasize that machine learning can help to lower the barrier in the middle offindability workflow and reduce energy or motivation to start the tedious work of metadata annotation.
Keywords. Machine learning, FAIR data, data curation, image tagging, digital catalyst.

1. Conceptualizing the Value of Data

4.

dynamics of values projected in the longIn the age of data-driven science and open

term.

science, data is a valuable asset. But what is theespecially

importantforinfrastructure and organization.

our conceptualization of the value of data intoThose types of values can be roughly mapped

four types; namely intrinsic value, basic value,

to existing jobs, but digital transformation

added value and persistent value.

requires re-definition of roles to take advantage

Intrinsic value refers to the value of raw data,of new methodology. In particular, this paper

such as how rare or how relevant it is to solve

focuses on machine learning in terms of its

a research question. This is especiallycontribution to increasing basic value and added

important for scientists and scholars from thevalue.

viewpoint of new findings and discovery, but
its usage heavily depends on the expertise.

3.

ispreservation of data with proper planning of

how can we increase them? Here we introduce

2.

This

archivists from the viewpoint of selection andbest way to characterize the value of data and1.

Persistent value refers to the temporal

Basic value refers to the organization of data,

2. FAIR Data and Findability

typically with good metadata and standard

In our definition, basic value is tightly connected

format. This is especially important forto the concept of FAIR, and in the context oflibrarians from the viewpoint of FAIRness totechnical innovation, findability is the focus ofmake data findable, accessible, interoperable

attention. Findability can be increased by humanand reusable.

labor such as annotating high quality metadata

Added value refers to the integration of data,

to the data, but this human labor is exactly thetypicallyandreason that prevents experts from doing it. How

presentation. This is important for curators

can technological innovation such as artificialfrom the viewpoint of creativity to illustrateintelligence (AI) increase the findability of data?

with

new

combination

Knowledge

new values created from combination, or toallow serendipity for unexpected discovery.

representation

is

the

first

approach. For example, Google launched “Google

50

photographs is mixed in time and space. On theDataset Search” in September 2018 to help findthe dataset on the web. This service asks people

other hand, general noun tags are useful forto assign tags defined in schema.org, which is anroughly grouping photographs by subjects,

interoperable schema across disciplines. Giving a

which

tag requires human labor, but it is much simpler

photographs.

isusefulforcomparing

multiple

Disaster photograph has similar characteristics,

than writing a full description, and is still an

because disaster is also a multi-faceted event. We

effective solution for increasing findability.
Machine learning is the second approach.

also realized that there is serendipity in automatic

Machine learning also requires human labor for

tagging. A tag given for a very different object iscreating training dataset, but it is done in

useful for grouping some types of objects that

advance, and it could be done by others.

share the same appearance.

Although machine learning’s final goal isGeneral noun has less value in archaeological

generalization to unseen data, the current

photograph, because a proper noun, or an entity,

technology works better on data similar with thesuch as ruins is the typical target of photographs.

training data.

Its space-time variability is also smaller than othercollections.

3. Tagging for General or Proper

Finally,

collectionofhistoricalphotographs is a promising target, because it

Nouns

captures people’s daily lives more than specificMachine learning has potential to be applied toobjects. What kind of subjects are more often

any numerical data, but the most attractive result

taken than others? This kind of research questionis currently obtained for general image data. To

could be quickly answered by automatic tagging

study the effectiveness of workflow to increase

approach.

findability, we prepared four types of image
collections, namely (1) ethnological field work
photographs,

the

(2)

archaeologicalfield

4. Machine

work

Learning

as

DigitalCatalyst

photographs, (3) disaster recovery photographsand (4) historical photographs. We then applied

The preliminary study suggests that machine

automatic image tagging [1] and had a brieflearning is not about the automation ofinterview with experts who created image

findability workflow, but works as “digital catalyst”

collections to compare the current result andto help human work. The term catalyst is inspired

their expectation. Among other lessons, we focus

from chemistry as a substance to lower thein this paper on the role of automatic tagging.

barrier in the reaction process and require less

We observed that automatic tagging is moreenergy to achieve the same result.

effective on the general noun than the proper

In a similar sense, the role of digital catalyst is

noun. For example, automatic tagging can give ato reduce high motivation energy requested for“person” tag, while human tagging can go further

experts to complete findability workflow, such as

to give person’s name as a tag. Typically, it ismetadata annotation. Basic tagging results at

considered that a tag of proper nouns, or entity

least provides a good starting point for highernames, is more valuable than general nouns, but

quality metadata given by humans. In this sense,

the story is not that simple.

machine learning can be considered as digital

Ethnological photograph is one example thatcatalyst to reduce the burden of human labor andgeneral noun works effective. In their field work,

allow humans to focus on higher quality work.

they record anything they think relevant, andlater try to organize them to find characteristics of

Acknowledgments. Photograph collections werethe local culture. In this case, a simple timeline

provided from the following collaborators: Dr.

organization is not useful because the subject of

Taku Iida in National Museum of Ethnology, Dr.

51

Yoko Nishimura in Toyo University, Ms. Hinako
Suzuki in National Research Institute for Earth
Science and Disaster Resilience, and Dr. Toshihiko
Kishi and his colleagues in Kyoto University.

References
1.

Krasin , I., et al. , OpenImages: A public
dataset for large-scale multi-label and multiclass
image
classification,
https://github.com/openimages, 201752

Embedding Machine Learning in the Data Life Cycle - An
Example from Minerals Exploration
Jens Klump1*, Jess C. Robertson1, Alistair White1, Francky Fouedjio-Kameny1,2, Ryan
R.P. Noble1, Nathan Reid1, Maciej Golebiewsky3, Ryan Fraser1
1* CSIRO Mineral Resources, 26 Dick Perry Avenue, Kensington, WA, 6151, Australia
2
Stanford University, 450 Serra Mall, Stanford, CA, 94305, USA
3
CSIRO Information Management & Technology, 36 Gardiner Road, Clayton, VIC, 3168, Australia
Email: jens.klump@csiro.au
Summary. Machine Learning (ML), as a data science tool, has the potential to greatly accelerate theinterpretation of data. However, data analysis and the use of ML tools are often applied only in the latestages of the data lifecycle. We used ML for identification of geochemical anomalies as part of our fieldwork.
To make a decision about which areas to target for further exploration, companies must first characterise the‘background’ chemistry to be able to robustly identify these anomalies. However, current workflows fromfield sample collection to lab analysis, to the production of background geochemical maps may take months.
By chaining together a suite of novel hardware, portable characterisation and machine learning workflows
we have reduced this initial part of the data lifecycle to 24 hours.
Keywords. machine learning, spatial data, mobile application, minerals exploration, geochemistry.
●

1. Introduction

A machine learning system deployed in thefield lab to generate updated maps, predict

The identification of chemical anomalies in

new measurements before they were taken,

potentially thick cover sequences has led to theand suggest locations for new samples.

discovery of large mineral deposits at depth. To
make a decision about which areas to target for
further

exploration,companiesmust

We demonstrate these efficiency gains in a

firstgeochemical survey situated in the remotecharacterise the ‘background’ chemistry to be

Nullarbor Plain in South Australia. Using theseable to robustly identify these anomalies.

tools and helicopter-based sampling we were

However, current workflows from field sample

able to collect and analyse samples and conduct

collection to lab analysis, to the production oftargeted infill sampling to generate regional

background geochemical maps take months. Bygeochemical and proxy-mineralogical maps ofchaining together a suite of novel hardware,soil and rock over an area of nearly 4000 km2

portable characterisation and machine learning

within seven days, carried out the next day at theworkflows we have reduced the turnaround time

field camp.

for preliminary geochemical data acquisition andanalysis to 24 hours. Our toolchain included:●●●

Digital metadata capture using tablets and a

2. FAIMS mobile app and IGSN

mobile server running a custom FAIMSFAIMS Mobile is a framework for low-cost,sampling app,customisable mobile and web applications toInternational Geo Sample Numbers (IGSN) forcollect data during field research in thesample tracking and management,

geosciences and other domains. FAIMS Mobile

Field-based sample preparation coupled withcan

optimised pXRF, andInterfaces, data structure, automation, validation,

integratemany

different

data

types.

and data export interfaces can be customised to53accommodate different research approaches andindustry to conduct field sampling campaigns

methods. All features work offline, with data

more efficiently while at the same reducing thesynchronising

devicesrisk of missing the next major discovery byopportunistically when a connection to a server isfollowing a statistically robust sampling strategy

available [1].

[3].

among

multiple

IGSN is a persistent, globally unique, webresolvable identifier for physical samples [2]. In

5. Conclusions

this project, we used it to identify and track

In this field sampling campaign, we combined

samples from the sampling event in the field andmobile data capture with on-the-fly data analysis

their analysis in the field lab, to their subsequent

into an uninterrupted processing chain. Thistransfer to storage at our home institution.

machine learning enhanced workflow lead to

The FAIMS mobile app was used with pre-significant

printed sample labels. The contextual information

savings

in

time

and

cost

for

conducting the sampling campaign and resulted

available allowed us to populate almost allin improved survey results based on a statisticallyelements of the sample description automatically,

robust sampling strategy.

resulting in significant time savings and improved
data quality. We reduced field sampling times

Acknowledgments.

from 20 minutes to 5-6 minutes per site for fiveThe

Coompana

field

campaign was supported by the Geologicaldifferent sampling media.

Survey of South Australia, IMDEX Ltd., andAerotech Australia. We thank the traditional

3. Geochemical field laboratory

owners for their permission to conduct thisSamples were prepared for analysis at asurvey on their land.

provisional laboratory set up in the fieldwork area.
For our preliminary analysis, soil samples wereReferences

dried, ground to a fine powder, and pressed into

1.

pellets. The pellets were analysed using a
portable X-ray fluorescence spectrometer (pXRF).
Streamlining the workflow reduced analytical2.

preparation and processing time to four minutes
per sample.
Since not all elements of interest can bedirectly detected with this method we used otherchemical elements present in the soil to act asproxies for the elements of interest. XRF analyses
are influenced by the structure of the analysed3.

material. We used a machine learning algorithm
to correct the pXRF measurements.

4. Data analysis
A probabilistic model used the relationshipbetween geospatial data and new pXRF datacollected on-the-fly. The results demonstrate abetterunderstanding

geochemistry,ofquantitative

theregional

assessmentofvariation and directed the infill sampling. These
workflows will enable the minerals exploration

54

Ballsun-Stanton, B., Ross, S. A., Sobotkova, A.,
Crook, P., FAIMS Mobile: Flexible, opensource software for field research. SoftwareX,
7, 47–52, 2018
Devaraju, A., Klump, J., Tey, V., Fraser, R., Cox,
S. J. D., Wyborn, L. A. I., A Digital Repository
for Physical Samples: Concepts, Solutions andManagement. In Kamps, J., Tsakonas, G.,
Manolopoulos, Y., Iliadis, L., Karydis, I. (Eds.),
Research and Advanced Technology for Digital
Libraries (TPDL 2017), Cham, Switzerland,
Springer International Publishing, 10450, 74–
85, 2017
Robertson, J. C., Cole, D., White, A. J. R.,
Fouedjio, F., Ballsun-Stanton, B., Noble, R. R.
P., et al., Accelerating minerals exploration
with in-field characterisation, sample tracking
and active machine learning. In GeophysicalResearch Abstracts, 20, EGU2018-4169, 2018

Data Archives for Social Science in Korea, Challenges andOpportunities
Hearan Koo1*
1* Korea Social Science Data Archive, Seoul National University Asia Center, Room 250, Gwanak-ro 1, Gwanakgu, Seoul, 08826, Republic of Korea
Email: hrkoo@snu.ac.kr

Summary. Open science has gained considerable attention in scientific communities in the past few years.
One of the key elements in open science is open access to research data. While it is not a new idea, therecent development in open data movement presents many challenges and opportunities for social science
data archives. This presentation addresses several issues on the sustainability of social science data archives
in Korea. It consists of two parts; the first part introduces social science data archives and their development
in Korea, particularly focused on KOSSDA (Korea Social Science Data Archive) and discusses the challenges
and opportunities faced by KOSSDA in the wave of open science movement. The second part talks about the
recent development of a social science data platform within Asian contexts, called NASSDA (Network of
Asian Social Science Data Archives). It concludes with few remarks on future roles and position of dataarchives in open science movement.
Keywords. research data, social science, Korea, Network of Asian Social Science Data Archives (NASSDA).

1. Introduction

leading data archives in Korea, started as a non-Open science has gained considerable attention

profit social science library in 1983. It transformed
to an integrated platform for quantitative andin scientific communities in the past several years.

qualitative research data and literature in 2006

While it means different things to different

and provides integrated online service of datastakeholders, open access to research data is one

and literature.

of the key issues in open science agenda. In social

One of the reasons for late development isscience disciplines, it is not a new idea. For a longdata culture. Data culture in Korean academic

time, research data in social science has been
openedandshared

with

broad

community was not favourable for data archives.

researchResearchers did not consider data produced bycommunities. Data archives have played a critical

public funds as a public good and were hesitantrole in providing digital platform for researchers

to share their data to re-use by others.

to open and share their own data.

There has been a significant progress in data

The recent surge in open science, however,publishing, since KOSSDA has established. Many

presents many challenges and opportunities forgovernment-funded research institutions andthe existing data archives. This presentation

other data-oriented organizations built their own

addresses several issues on the sustainability ofdata archives in Korea.

repositories. Open Data Portal and Korean

2. Social Science Data Archives inbegan to service data online. However, there is

Research Memory, managed by government,
little reward to be given to data sharing and data

Korea

publishing does not gain the status of a scientificCompared to Europe and US, social science data

publication yet.

archives emerged relatively late in Korea. KOSSDA

Meanwhile, the demand for open data is on(Korea Social Science Data Archive), one of thethe rise and open data policy evolved rapidly.

55searchable, visible, and re-usable is crucial for the

3. Challenges and Opportunities

future of data archives.

The recent development in open science andopen data movement presents the number of

4. International networking

challenges and opportunities for data archives

As part of extending data curation services,

3.1 Self-publishing vs. professional archiving

international networking is recognized for its

The current open data movement facilitates self-

importance. KOSSDA becomes aware global

publishing of research data. A lot of open data

nature of research data and the necessity ofplatform such as Dataverse provide automatedcoordination of data services in an international

tools and services for self-archiving which havecontext.
KOSSDA has been actively involved in thelong been provided by professional archivists.
easy

discussion of founding NASSDA (Network of

publish without expert help and allows full

Asian Social Science Archives) with representative

Self-publishingenables

quick

and

control over researchers’ own data. On the otherarchives of three other countries – SSJDA in Japan,

hand,

CNSDA in China, and SRDA in Taiwan.

professionalarchivingguarantees

consistent and secured archiving and assures

NASSDA was born in 2016. It aims to build aquality of published data. Although different

sustainable infrastructure for social scienceways of archiving have their own merits,

research in Asian regions, which is intended to beresearchers can choose various data publishingan open platform of sharing and distributing

options and it becomes a potential threat to thesocial science research data.

job of professional archivists.
3.2 General repository vs. domain repository

5. Conclusions

There are various kinds of repositories (CESSDA,

Social science data archives are facing critical2017). General purpose repository piles up ofchallenges in the wave of open science and open

numbers

scientific

data movement. But challenges can become

disciplines. It accepts a wide range of data types

opportunities. As open science continues toand is suitable for cross-disciplinary data. Domain

evolve, it will restructure data ecosystem in Korea.

specific repository, on the contrary, tends to

Researchers and funders will acknowledge theaccept limited types of data and to focus more on

values of data sharing and cooperate tospecialized data management.

implement appropriate open data policy. The

of

data

from

different

It is reported that using domain-specificsuccessful introduction and enforcement of arepository for data sharing is not a preferreddata sharing policy will support the job of data

option in social science disciplines. Data archives

management and curation. More innovativeneed to devise more incentives that induce

efforts of data archives are needed to maintain aresearchers to choose data archives for their open

stable position in data ecosystem.

platform.
3.3 Repository vs. curation

References

According to Wiley’s survey in 2016, the primary1.

motivation behind data sharing is to increase theimpact and visibility of one’s research (CWTS,
2017). In this respect, data curation is essential.

2.

Although repository offers search, navigate,
and visualize functionalities, data curation in
general repositories is limited. Data archives has
invested a lot of efforts to curate and promote
the re-use of data. Making published data

56

CESSDA Training Working Group, CESSDA
Data Management Expert Guide, Bergen,
Norway: CESSDA ERIC. Retrieved fromhttps://www.cessda.eu/DMGuide, 2017
Centre for Science and Technology Studies(CWTS), Open data: The Researcher
perspective, Elsevier, 2017

Toward universal information accesson the digital object cloud
Kei Kurakawa1*
1* National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
Email: kurakawa@nii.ac.jp

Summary. Universal information access on the digital network has been a long-lasting objective since thedawn of the Internet age. One of the technologies to aim at the universal information access is Digital Object
Architecture proposed by Kahn and Wilensky in late 1980s. RDA (research data alliance) as a professional
data community founded in 2013 re-focuses on this technology and intends continuous development andadaptation for scientific data. The paper describes the technological core information management andaccess scheme discussed in RDA leading to Digital Object Cloud as a promising next generation data world.
Keywords. Universal information access, digital object cloud, persistent identifiers (PID), PID centric data
management and access.

1. Introduction

specific data exist in the heart of the community

Universal information access on the digitalas follows. They are as follows.
•

network has been a long-lasting objective sinceComputational data scheme should not be

the dawn of the Internet age [1]. In 1945,

complicated, ever-lasting, and independent

Vannevar Bush proposed the first idea ofon computer technology changes.
•

document management system. In 1960s, Ted

Dataformatanddata

attributes

arecomplicated at some professional levels.

Nelson introduced topics of hypertext, and Doug•

Engelbart implemented the ideas on working

Only the professionals can deal with dataprocessing and management.

systems. The World Wide Web was invented by•

Tim Berners-Lee in late 1980s as covering these

Of course, the professionals have goodideas of knowledge representation on theknowledge of the domain.

Internet. Digital Object Architecture [2] was

In

the

context

ofthecommunity

proposed by Kahn and Wilensky in late 1980s to

understanding of the data management, the RDA

manage and access the digital objects on themainly focuses on the Digital Object ArchitectureInternet. These technological inventions continue

and enhance it to adopt for each disciplinary data

to evolve into state-of-the-art information access.

management so as to share the data for everyone.

A professional data community, RDA (researchThis paper illustrates the idea of thedata alliance) was established in 2013 andenhancement of the Digital Object Architecture

launched biannual plenary meetings to develop

discussed in the RDA technical community

community

amongconsensus

andspecifications

on

the

management

andaccessdisciplinary fields.

technologicalprofessional
among

which

information

scientists

and

data

engineers believe that Digital Object Architecture

multi-

is the best way to universal information access for
those professional data communities.

In the professional data

community of scientific experimental research,such as geoscience, life science, material science,

2. Digital Object Cloudexperimental fields of natural language, several

Digital Object Cloud (DOC) is the universal

premises to manage and process the domain

information access platform vision on the basis of57

following

types. In the second step, it provides data withsubsections explain elements of the component.

data types. Needless to say, the last step is

2.1 Digital Object Architecture

inevitable for the automation.

Digital Object Architecture [2] consists of the

2.4 Data discovery

following elements; Digital Object (DO), Handle (aAutomatic data processing and data discovery

unique, persistent identifier (PID)), Handle System,

are different tasks. Metadata to achieve those

DO repositories, DO registries. DO is the central

tasks are separately managed. Dataset search

object that is any unit of information represented

engine such as Google dataset search (GOODS)

in digital form, may be structured as a digitalconsiders organizing datasets from search engine

object within the Internet. The structure of a DO,

perspectives

including metadata, is machine and platform

metadata to users [7,8].

DigitalObjectArchitecture.

The

finallytoprovidecatalogue

independent. Every DO has a unique persistentidentifier or a Handle. The Handle Server is the

3. Conclusions and Future Work

resolution system that maps handles to state

Universal

information of the digital object which includes

information

accessisageneralrequirement for all people who imagine thelocation and others. DO can be stored in DO

digital world of the Internet. RDA deal withrepositories, and DO registries let DOs to betechnologies for this, especially Digital Object

federated among DO repositories.

Architecture.

2.2 Virtual layer for information access

This

paper

illustrated

recenttechnical developments emerged in the RDA. We

DOC exists on the basis of the Digital Object

hopefully continue to develop this kind ofArchitecture [3]. It consists of the layered

technologies and adapt it for real professionalarchitecture, which are, from top to bottom,

data cases.

network of digital objects, network of internet
devices, and computing and storage. The

Acknowledgments. The author is thankful for

network of digital objects is virtually layered on

RDA Kernel Information WG discussions.

the network of internet devices with its
computing and storage. Users intend to get

References

access digital objects by PID on the virtual layer.

1.

2.3 PID centric approach to data management
and access
PID is a key to get access to the digital object on

2.

the DOC. We assume that millions of DOs are
spreading out on the DOC. To automate every
process for the DOs, such as collecting, evaluating,pre-processing, calculating, publishing DOs, weneed to know data types of DO and relationship
attributes among DOs. Data type registries storessuch the data types of DOs [4]. A local Handle

3.

server pushes relationship attributes among DOs,
we call it kernel information, in response to theclient which requests the state information of DO

4.

by a PID [5].
For automating data processing, data typing isa necessary task. To proceed this task, a data

5.

provider has two steps [6]. In the first step, it
prepares data and its documents about data

58

Denning, P. J., Kahn, R. E., The long quest foruniversal
information
access.
Communications of the ACM, 53(12), 34. DOI:
10.1145/1859204.1859218, 2010
Kahn, R. E., Wilensky, R., A framework for
distributed
digitalobjectservices. International Journal on Digital
Libraries 6, 2, DOI: 10.1007/s00799-005-0128x. (First made available on the Internet in1995 and reprinted in 2006 as part of acollection of seminal papers on digital
libraries), 2006
Lannom, L., Wittenburg, P., Global DigitalObject Cloud (DOC) - A Guiding Vision.
http://hdl.handle.net/11304/a8877a1a-9010428f-b2ce-5863cec4aff3, 2016
Broeder, D., Lannom, L., Data Type Registries:
A Research Data Alliance Working Group. DLib
Magazine,20(1/2),
DOI:
10.1045/january2014-broeder, 2014
Weigel, T., Plale, B., Parsons, M., Zhou, G., Luo,Y., Schwardmann, U., Quick, R., Hellström, M.,
Kurakawa, K., RDA Recommendation on PID

6.

KernelInformation.
https://www.rdalliance.org/sites/default/files/RDA%20Reco
mmendation%20on%20PID%20Kernel%20In
formation.pdf, 2018
Kurakawa, K., Sekiya, T., Baba, Y., Making datatyping efforts or automatically detecting datatypes for automatic data processing?
Research Data Alliance 11th Plenary Meeting,
Berlin,Germany,
2018.03.21-23.
https://www.rd-

7.

8.

59

alliance.org/sites/default/files/rda11_poster_
20180321_kurakawa.pdf, 2018
Halevy, A., Korn, F., Noy, N. F., Olston, C.,
Polyzotis, N., Roy, S., Whang, S. E., Goods:
Organizing Google’s Datasets. Proceedings ofthe 2016 International Conference on
Management of Data - SIGMOD ’16, 795–806,
DOI: 10.1145/2882903.2903730, 2016
GoogleDatasetSearch,http://g.co/datasetsearch

SPEDAS (Space Physics Environment Data Analysis Software):Multi-mission Heliophysics Data Management, Analysis,
Visualization, and Collaboration
James W. Lewis1*
1* University of California, Berkeley, Space Sciences Laboratory, 7 Gauss Way, Berkeley, California, 94720, USA
Email: jwl@ssl.berkeley.edu

Summary. SPEDAS (Space Physics Environment Data Analysis Software) is a software framework andcollection of tools developed to support heliophysics research. Its core features include the ability to
download data from remote servers as needed, management of a local cache of downloaded data, a richcollection of analysis and modelling tools, and extensive support for time-series plots and other types ofdata visualization. In addition to the core, general-purpose tools, SPEDAS includes a plug-in architecture topermit rapid development and deployment of modules to support new missions, data types, andvisualizations. By adopting an open-source development philosophy, adhering to recognized interfaces andmetadata standards where possible, and implementing a robust quality assurance program, SPEDAS has
emerged as a key enabling technology of the multi-mission Heliospheric/Geospace System Observatory(H/GSO) concept.
Keywords. Heliophysics Software Analysis Visualization Collaboration.

1. SPEDAS development history andexpressed interest in modifying TDAS to suit theirneeds. That effort eventually led to the initial

motivation

release of SPEDAS, where the emphasis of the

In the early 1990s, researchers at the UC Berkeley

software was on the general-purpose, multi-Space Sciences Lab (SSL) [1] developed a set of

mission capabilities, and TDAS became one ofsoftware tools, written in the IDL programming

several plug-in modules that leveraged thelanguage, to support the multi-instrument data

general-purpose SPEDAS framework to meet theanalysis needs of the FAST satellite mission. [2]needs of specific missions.

These tools (notably, the TPLOT package forplotting time-series data), were shared informally

2. SPEDAS capabilities

among researchers in the field, and continued to

SPEDAS provides many of the tools and services

evolve.

needed to accomplish a typical workflow in

In the early stages of the THEMIS (Time History

heliophysics research:of Events and Macroscale Interactions during•

Substorms) mission, the science team adopted

Automaticallydownload

data

from

this collection of tools as the preferred data

remote servers and manage a local data

analysis environment for both the space-based

cache
•

segment, and a complementary ground-based

Perform coordinate transformations andother analysis operations

segment (a network of magnetometers and all•sky imagers). The THEMIS version of these tools

Interface with GEOPACK magnetic fieldmodelling library

was released as TDAS (THEMIS Data Analysis•

Software).

Use

HAPI

(Heliophysics

Application

As researchers gained familiarity with TDAS,

Programming Interface) or NASA’s SPDF

other projects with similar data analysis needs(Space Physics Data Facility) [3] to locate60

•and download data from a large

sources and missions, in a single, unified

selection of archived data sets

environment. Its ease of use, open approach toGenerate publication-ready plots using

software development and distribution, and thethe command-line TPLOT package, ordevelopment team’s commitment to provide athequality software product to the widest possible

SPEDASGUI(GraphicalUser

audience, have led to its adoption by several

Interface)other projects. The focus on multi-mission

3. SPEDAS

development

supportandhas

provided

fertilegroundforcollaboration between missions, and achieving

distribution strategy

the goals of the Heliophysics/Geospace System

SPEDAS is under nearly continuous development,

Observatory.

and enhancements and bug fixes are checked
into the source code repository on a daily basis. A

Acknowledgments. The author is grateful to Dr.

nightly snapshot of the source code repository is

Yoshimasa Tanaka, Dr. Masaki Kanao, and themade freely available for download from thelocal organising committee for travel support and

SPEDAS web site [5], for users who want access tothe opportunity to participate in this conference.

the latest features.

This work was supported by NASA contracts

On a semi-regular basis (1-2 times per year),NAS5-02099 and NNG17PZ01C.

formal SPEDAS releases are built, thoroughly
tested by the SPEDAS core development team,

References

and made available to the community.

1.

To make SPEDAS available to the broadest
possible audience, for every formal release, theSPEDAS team builds a set of VM (Virtual Machine)2.

executables for Windows, Linux, and Mac, which
are free to download and do not require an IDL
license. Although the executable releases do notsupport access to the IDL command line, most of3.

the key SPEDAS features are exposed via the GUI,
so the free executable release is a viable option

4.

for many researchers and students.
5.

4. Conclusions
SPEDAS enables the loading, analysis, andvisualization of data sets from many different

61

University of California, Space Sciences Lab,
http://www.ssl.berkeley.edu/[September
2018]
McFadden, J. P., Ergun, R., Carlson, C. et al.,Science Operations and Data Handling for
the FAST Satellite, Space Science Reviews,
98,169-196, 2001
Space
Physics
Data
Facility,
https://spdf.gsfc.nasa.gov/ [September 2018]
Faden, J. B., Weigel, R. S., Merka, J. et al. ,Autoplot: a browser for scientific data on theweb, Earth Science Informatics, 3, 41-49, 2010
SPEDAS, http:/www.spedas.org [September
2018]World Data Center for Microorganisms: The global cooperation
on microbial big data
Juncai Ma 1*
1* Institute of Microbiology, Chinese Academy of Sciences,NO.1 Beichen West Road, Chaoyang District, Beijing
100101, China
Email: ma@im.ac.cn

Summary. Microbial resources are one of the most important natural resources in the world, which is thescientific basis to support the development of biotechnology and life sciences. WFCC-MIRCEN World Data
Centre of Microorganisms (WDCM), which once hosted in Australia and Japan and then moved to China in
2010, plays a crucial role in providing a database of microorganisms, analysis of the function and establishinga platform of international communication. WDCM launches the international project Global Catalogue ofMicroorganisms (GCM) to construct a data management system and a global catalogue to help organize,
unveil and explore the data resources of culture collection worldwide. Now 112 international culturecollections from 43 countries and regions joined GCM. GCM provides a comprehensive view on the
microbiological material made accessible online by public collections, and the function of Analyzer of Bioresources Citation. Future developments such as “BIG DATA” technology including semantic web or linked
data will allow the system to provide more flexible data integration broader data sources. Linking WDCM
strain data to broader data sets such as environmental, chemistry and research literature can add value todata mining and targeting microorganisms as potential sources of new drugs or industrial products.
Cooperation with other organizations and institutions promoted broad utilization of WDCM data platform.
The WDCM is exploring collaboration with the World Health Organization (WHO) for the establishment of adatabase allowing influenza virus information integration. Moreover, the WDCM database is able to provide
services for the implementation of the TRUST code of conduct to allow culture collections to comply with
the Convention on Biological Diversity and Nagoya protocol for Access and Benefit Sharing. The uniquestrain identifier available through the WDCM will and the further utilization of information extracted by ABC
implements key provisions of the Nagoya Protocol and provides required transparency, legal certainty while
lowering transaction costs and reducing administrative and governance burdens. The WDCM will work with
Research Infrastructures, Publishers, Research funders, Data holders and individual collections and scientists
to ensure data interoperability and provide the environment for enhanced tools for research anddevelopment. WDCM is prone to evolve and continue. WDCM now started the development of the data
platform to microbiome. Recently, WDCM announced the launching of Global Microbial Type Strain Genome
and Microbiome Sequencing Project in the 7th WDCM Symposium, marking the GCM project has begun toenter a new stage (GCM 2.0). Focused on exploring the genomic information of microorganisms, this project
has planned to sequence all uncovered prokaryotic type strains together with select eukaryotic type strains,construct a database for genomics data sharing, and also provide online data mining environment. Working
groups responsible for selecting bacteria and fungal strains, drafting SOP, managing intellectual property
right and legal issues and constructing database have already embarked on the pioneer stage of GCM 2.0.
The project will establish a cooperation network for type strain sequencing and functional mining, andcomplete genome sequencing of over 10000 species of microbial type strains in five years.

Keywords. WDCM database, GCM project, microbiome, BIG DATA, TRUST code.

62Extended Cell Suppression Problem
Towards Better Data
Kazuhiro Minami1*, Yutaka Abe2
1

* The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo, 190-8562, Japan
2
National Statistics Center, 19-1 Wakamatsu-cho, Shinjuku-Ku, Tokyo, 162-8668, Japan
Email: kminami@ism.ac.jp

Summary. The cell suppression problem (CSP) for tabular data has been studied in the research communityof official statistics for many years. In CSP, we usually suppress regular internal cells to marginal cells because
information on marginal sums is more likely to be available elsewhere. We, however, find a class of tables
with no cell suppression pattern for regular cells that protects sensitive cell values properly. Such a situation
occurs when a small value of a marginal sum restricts the upper bound of each cell contributing to that sum
narrowing the ranges of their possible values. To study positive aspects of suppressing marginal cells in CSP,
we compare the approach of suppressing both marginal cells and regular cells with that of suppressing only
regular cells after preprocessing unsafe rows and columns. Our initial experimental results show thatsuppressing marginal cells is an effective way of reducing information loss in terms of the number ofsuppressed cells.
Keywords. cell suppression problem, data utility, statistical disclosure control.

1. Introduction

columns, which we cannot ensure enoughuncertainty of values for primary suppressed cells.
After the preprocessing, we perform secondarycell suppression on regular internal cells. The
other approach is to include marginal cells as
possible targets for suppression.
In this paper, we experimentally evaluate thetwo approaches using tabular data produced
from the official survey ``National Survey of
Family Income and Expenditure [3]. Our
experimental results show that to suppress
marginal cells not only solves the issue of unsafe
tables and but also reduces information loss ofthe original table effectively.

The cell suppression problem (CSP) [1] for tabular
data has been studied for many years. The goal of
CSP is to find the minimum set of secondary
suppressed cells (i.e., a suppression pattern) that
ensures sufficient uncertainty on the value every
sensitive cell under the presence of linear
relationships concerning marginal sums.
In CSP, we usually suppress regular internal
cells to marginal cells because information on
marginal sums is more likely to be available
elsewhere. However, when we apply a SDC toolfor CSP [2] to tabular data produced from theofficial survey ``National Survey of Family Income
and Expenditure,'' in Japan [3], we find that, if wesuppress only regular internal cells in a table,
there exist a class of tables with no feasible
suppression pattern for a safe table. Such asituation occurs when a marginal sum restricts
the upper bound of each cell contributing to thatsum narrowing the ranges of their possible values.
smaller than a given threshold value.
We consider two approaches to address thisissue. One is to perform preprocessing on an
input table to remove ``unsafe'' rows and2. BackgroundFigure 1 shows an overview of the CSP algorithm.
The algorithm takes an original table and security
parameters as inputs and outputs a suppressedtable and its corresponding suppression pattern,which is a binary matrix that specifies the
positions of suppressed cells. There are two
security parameters in the input.

frequency threshold 𝑡 is used for primary
suppression.

63

The unitthe public survey data of National Survey of
Family Income and Expenditure [3] in 2014. The
sample size of the survey is 51,768 households,
and their geographical areas in Japan are divided
into ten regions. We prepare frequency tables of
the households by crossing geographical regions
and exact age of a head of household in several
different ranges.
The bar graph in Figure 3 compares the total

Figure 1: An algorithm for cell suppression problem (CSP)

number of suppressed cells with the two

The algorithm primarily suppresses cells whose

methods. This results shows that the method ofvalue is smaller than 𝑡. The second parameter is asolving the extended CSP performs better thanconfidentiality interval threshold, which is a pair

the preprocessing method in terms of data utilityof two values 𝑙𝑝𝑙 and 𝑢𝑝𝑙 , which ensures that

because the numbers of suppressed cells areeach primary suppressed cell has a sufficient

smaller than those of the preprocessing method

range of possible values as shown in Figure 2.

by 6% to 20% respectively.

Figure 2: Confidentiality interval

3. Issue of no feasible solution
There are two cases where there is no feasible
suppression pattern for tabular data. The first

Figure 3: Comparisons of suppressed cells in total. The
bars for the new method consists of the counts ofinternal cells and marginal cells with different colors.

case is that a marginal sum 𝑠 is smaller than an
upper bound threshold 𝑢𝑝𝑙 l. Then, the upperbound of every cell belonging to that marginal issmaller than 𝑢𝑝𝑙, violating the condition in Figure

References

2. The second case occurs when a difference
between the cell value 𝑎𝑝 of a primary1.

suppressed cell and its marginal sum 𝑎𝑠𝑢𝑚 issmaller than 𝑢𝑝𝑙. If this is the case, 𝑎𝑝 = 𝑎𝑠𝑢𝑚 <𝑎𝑝 + 𝑢𝑝𝑙 holds

againviolatingthesafety2.

condition.

3.

4. Experimental results
We evaluate two approaches to address the issue
in Section 3. One is to perform preprocessing on
an input table to remove unsafe rows andcolumnsandthenperform

secondary

suppression on internal cells. The other is tosuppress marginal cells as well as internal cells.
We use a set of frequency tables produced from

64

Castro, J., Recent advances in optimization
techniques for statistical tabular dataprotection. European Journal of Operational
Research, 216, 257–269, 2012
Minami, K., Abe, Y., Statistical disclosure
control for tabular data in r. Romanian
Statistical Review, 4, 67–76, 2017
National survey of family income andexpenditure,
http://www.stat.go.jp/english/data/zensho/index.html

Amenability of the United Nation’s Sustainable Development
Goals to Big Data Modelling
Kassim S. Mwitondi1*, Barnabas N. Gatsheni2 and Isaac Munyakazi3
1*Sheffield Hallam University, Faculty of Science, Technology and Arts; Sheffield S1 2NU. United Kingdom

2

University of Johannesburg, Dept of Applied Information Systems, PO Box 524 Auckland Park 2006 RSA
3Ministry of Education, KG 7 Avenue, Kigali, Republic of Rwanda
Email: k.mwitondi@shu.ac.uk

Summary. As the world gets increasingly entrenched into the information age, winners and losers aredefined by levels of connectivity and utilisation of the potential knowledge that flows along shared
networks. This paper examines the challenges and opportunities the developing world faces in attaining theUnited Nation's Sustainable Development Goals (SDGs). It explores the potential for converting SDGembedded attributes into measurable data attributes amenable to Big Data modelling for socio-economictransformation. Assuming accessibility of locally authoritative data, interdisciplinary skills, infrastructure andrelevant policies, each SDG is viewed as a data source and user node with measurable attributes. Attainmentof the goals depends much on the strategies each country puts in place for harnessing and modelling thehighly dynamic interactions of the data attributes associated with SDGs. This approach leads to the widely
published, but probably still lacking ground level practical realisation, concepts of Big Data and Data Science.
Based on theoretical implementations of unsupervised and supervised modelling, the paper proposes a
Development Science Framework (DSF) for integrating and modelling data attributes harnessed from SDGs.
The power of the proposed framework derives from the interaction between the inner layer - i.e.,
formalisation of the nature of relationships among the highly correlated, multivariate attributes across theset of nodes and the outer layer, a governing shell at each node. We show that sustainable attainment of theSDGs is a coherent phenomenon that is independent of the individual goals and heavily depends on these
relationships and the utilisation of the knowledge from the integrated modelling environment. Real socioeconomic data are used to validate the mechanics of an implementing algorithm in addressing data
modelling inconsistencies and variability. Illustrations focus on interdisciplinary shared approaches to
identifying what works, via which we expound the complexity of SDGs data and grey literature.
Keywords. Big Data Modelling, Data Science, Development Science, Sustainable Development Goals, Value andMeasurable Objectives.

1. Backgrounddeveloping

In 2015 the United Nations member states signed

promoting interdisciplinary collaborative work.

relevant

tools

and

skillsandBased on theoretical implementations of

up to 17 SDGs spanning across various aspects ofunsupervised and supervised modelling, thelife. Each goal was defined with measurable aims

paper

for improving human quality of life, via targets

proposesaDevelopment

Science

Framework (DSF) for integrating and modellingand indicators set to be achieved by 2030 [1]. The

data attributes harnessed from the SDGs. It

UN Statistics Division is responsible for collecting

highlights challenges and opportunities forand cataloguing data from member states for

exploiting inherent knowledge in data attributes

monitoring progress-which is quite a big ask,

as described in [3]. The DSF presents a generalgiven the dynamics and disparate standards andframework

policies across member states [2]. Addressing

inspired

by

data-

based

considerations of SDGs attainment in a triangularthese challenges, requires adaptive capacities in65relationship of authoritative data availability,

the surrounding large volumes of data to usefulinterdisciplinarity and development. Its main idea

knowledge? We set the following objectives.

derives from viewing each SDGs in Figure 1 as a

1.

data

source/user

node

with

multi-faceted

General: Knowledge–based layout for SDGs.
1.1. To

attributes and dynamics to be harnessed, stored

propose

a

wide

range

of

development data sources, including

and modelled in co-ordinated strategies withgrey literature.

other nodes, and leading to attainment of Vision,

1.2. To determine the natural path of data

Values and Measurable Objectives (VVMO).

flows and how they can be followed.
1.3. To establish how multiple interacting
agents can identify the key attributes,
understand how they interact andappreciate their impact on societies.
2.

General: Knowledge Extraction from Data.
2.1. To highlight theoretical and practicaljustifications for development based oninherent information in SDGs attributes.
2.2. To outline the requirements for creatinga Development Science Framework
index.

Figure 1: Sustainable Development Goals

2.3. To propose an indexation prototype
usingThis framework aligns with the widely

SDGs’

descriptive/inferential

parameters.

published, but probably still lacking ground-level

Fulfilling the foregoing objectives hinges onpractical realisation, concepts of Big Data andData Science. Thus, in carrying out the analyses,

knowledge

we follow the scientific approach proposed in [4],described in [8] as requiring not only technicalattributing science to a system of postulates andbut also cultural adaptation. In other words, KM

statements and sequentially testing them against

involves, inter–alia, associating each SDG note in

actualandFigure 1with people, time, locations, resources,

experiments. The proposed framework traverses

organizations and conditions–forming a highly

across the SDGs, providing spatio-temporalcomplex source of knowledge, often referred tocomparative examples.

as entity extraction.

experiencevia

observations

management(KM)–aprocess

The SDGs constitute a complex multivariate

1.1 Motivation

dataset with highly interacting variables. For

The link between computational intelligenceexample, the 4th SDG, Quality Education, focusesand development is discussed in [5-7]. We set off

on access, inclusivity, universality as well asfrom the reasoning that variations in data andwomen and girls, among others. The goal alsomodelling outcomes arise from estimation ofidentifies Sub-Saharan Africa, a region also

sources of knowledge, hence the need forcharacterised by a high percentage of young

identifying the key attributes; understanding how

population and high birth rates, as mostthey interact and appreciating the impact theystruggling to access education. Many more

have on societies. These requirements are

attributes can be listed within this node andfundamental in the modern era in which theequally many can be directly or indirectly

potentials to harness and make use of data areassociated with attributes in the remaining 16

increasingly outpacing capacities.

nodes. It is in this context that we envision SDGs

1.2 Research Question & Objectives

as a Big Data problem and, consequently, expect

The paper seeks to answer the following

countries and institutions to identify and manage

question. How can developing nations convert

66

SDG-node related attributes for knowledge
extraction purposes.
The paper is organised as follows. Section 2
outlines the methods–DSF structure, data sources
and implementation strategy. Section 3 provides
illustrations based on real data and discusses
issues relating to data randomness and variation.

Figure 2: Layered structure for SDGs Big Data Modelling

Finally, concluding remarks are made in Section 4.

The Inner Layer is multifarious and highly
complex–it

2. Methods

multivariate

data

functional and hidden relationships that must be

indicators and, as such, they naturally lead to

uncovered,critical questions and issues. Some of raisedmonitored

andappropriately

adopted as inputs in enhancing the SDGs. The

issues relate to the role of KM in Governments [9].

two sub–structures operate on the global fabric,Thus, the DSF consists of multiple layers of highly

influenced

disparate, and often fragmented, data that leadobjectiveof

attributes from all the SDGs, interactions,

The SDGs in Figure 1 entail multiple targets andtocomprisesbyseveralexternalfactorsasillustrated in Figure 2.

identification, monitoring andadjustment of the Values and Measurable
Objectives (VVMO).

2.1 Data SourcesData

sources,

rated

by

comparative

amenability to their SDGs Big Data Modelling, arein Table 1.

Figure 3: A graphical illustration of SDGs data sharing
Table 1: Amenability of SDGs Big Data Modelling

Figure 3 illustrates a typical SDGs data

2.2 Implementation Strategy

communication mode required for implementing

The strategy is to treat SDGs-related data

the structure in Figure 2, bearing in mind theattributes as forming two strongly correlated

ratings in Table 1. Together they define two main

layers-the outer and inner layer (Figure 2). The

components of DSF–design and implementation.

fabric on which the SDGs nodes operate

Both design and implementation require as

epitomises governance and forms the outer layer-

inputs, authoritative data, tools and techniques,a voluminous and high–dimensional data node,skills and resources–all governed by policies andlargely a function of the prevailing internal andother non–inner layer factors. It is imperative toexternal political spectra. It plays an extremely

recognise these as part of the data attributes tovital role in attaining the VVMO. Its core function

be modelled–not least because they are allis to clear perturbations, crises or disagreements–

embedded in one or more SDGs. We set off fromi.e., VVMO support through the prevailingthe premises that development is a function ofpolitical spectrum.

successful SDGs–a reasonable assumption for our
research question. Hence, to determine the main
drivers of SDGs, we focus on the SDGs-related

67

data tasks, tools, techniques and features in Table

Due to the complexity of indexation, a study

2.

aimed at improving an SDG phenomenon may
consider variables from sources as diverse as
brainstorming, pilot projects, past experiences orlessons from elsewhere, most of which may behighly correlated. Table 3 presents few selected
driver–variable examples for SDG #4 and SDG #9
to illustrate the level of complexity involved.

Table 2: Selected examples of SDGs Big Data modelling
tasks, tools, techniques and measurability features

It is also reasonable to assume that both theGross National Product per Capita (GNPPC) andthe Human Development Index (HDI) in a country

Table 3: Selected driver–variables for SDG #4 and SDG #9

reflect the country’s productivity, which, on the

2.2.2

other hand, is a function of the 4th SDG, Quality

To optimally partition Ω we must identify 𝑘

Education and the 9th, Industry, Innovation and∗vectors 𝑥1∗ , 𝑥2,…,
𝑥𝑘∗ ∈ ℝ𝑛 that solve the function

Infrastructure. Clearly, the two SDGs are also
highly correlated and so we need to pay attention
Indexation and Visualisation

Given relevant data, we can extract related

andparameters and use them as proxies for the level𝑓�𝑥1 , 𝑥2,…, 𝑥𝑘 �

if

let

we

(3)

𝑝

= � 𝒟𝑗 �𝑥1 , 𝑥2,…, 𝑥𝑘 �
𝑗=1

anindicator

variable

𝑧𝑖=1,2,…,𝑛 , denote group membership, we canof development in numerical or graphical formats.

search for optimal values through iterative

For instance, if we denote SDG–relevant variables

smoothing of the random variable 𝑥|𝑧 = 𝑘 forbyΩ = �𝑥𝑖𝑗 � 𝑖 = 1,2, … , 𝑛 & 𝑗 = 1,2, … 𝑝

min𝑥1 , 𝑥2,…, 𝑥𝑘 ∈ℝ𝑛to multicollinearity in analysing data from them.
2.2.1

Unsupervised & Supervised Modellingwhich we compute the parameters 𝜃.

(1)where 𝑛 ≫ 𝑝, then, the parameters associatedwith Ω can either be computed from data

3. Conclusions

samples–if distributional priors and densities are

Prior to looking into causes and modes ofknown, or they can be estimated from data.

improvement for any process, we must have a

Without loss of generality, assumed that any

good understanding of experiences from the

𝑥𝑖𝑗∗ ∈ Ω; i > 𝑗 ≥ 2 are

past–immediate and distant. The iterative natureassociated

with

the

of Figure 2 implies that to generate descriptive or

parameters 𝜃 = �𝜇̂ 𝑖 , 𝜎�𝑖 , 𝜌�𝑖𝑗 �.

inferential parameters, improvement requires

One common example is the Humanthat we determine what previously worked well

Development Index (HDI) in its computational

and what did not. Answers to such questions leadvariants [10], a composite function of Life

to further questions such as to how previous

Expectancy (LE), Educational Attainment (EA) andstrategies can be improved upon. The answer toStandard of Living (SL). One variant fixes thethis question can only be obtained if it is known

annual maximum & minimum values of eachas to why there were failures, if any. Finally, andindicator and deduces the Achievement Level

most importantly, answers to all the foregoing(AL)
𝐴𝐿𝐿𝐸 =

𝜇𝐿𝐸 − min 𝐿𝐸⇒ 𝐻𝐷𝐼
max 𝐿𝐸 − min 𝐿𝐸
𝐴𝐿𝐿𝐸 + 𝐴𝐿𝐸𝐴 + 𝐴𝐿𝑆𝐿=
3

questions are data–driven and, quite often, they(2)

lie uncollected in fragmented data files, project
reports and grey literature. A quick inspection of
selected open data sources and repositories in68

Table

1

shows

that

data–driven

8.

SDG

Parsons, M., Godøy, Ø, LeDrew, E., de Bruin, T.

implementation is more likely to succeed if driven

Danis, B., Tomlinson, S., Carlson, D. A.,

by data that are locally harnessed and utilised.

Concept. Fram. for Manag. Very Diverse Data

Thus, while many multilateral institutions hostfor Complex, Interdisciplinary Science. Journ.

global data on their servers, optimum benefits toof Inform. Science, 37, 555-569, 2011
9.

local communities can only be achieved if locally

Liebowitz, J., Will Knowledge Manag. Workauthoritative data sources and repositories arein the Govern.? Elect. Govern. an Intern.

used.

Journ. , 1, 1-7, 2004

Limitations

onmodellingand10. Jahan,domainS.,

Jespersen,

Human

Everyone.

Humanknowledge skills are common, and one practical

Development

example of how this could be addressed is forDevelopment Report, UNDP , 2016

Governments, the UN and other multilateralorganisations

tosupportnational

level

development Data Science Centres by running
regular free webinars hosted by interdisciplinary
teams.
Acknowledgments. We are grateful to the
National Institute of Polar Research for inviting us
to this workshop and funding the participation of
one of the authors. We are also thankful to ourcolleagues

atSheffield

HallamUniversity,

University of Johannesburg and the Ministry of
Education in Rwanda for allowing us time toconcentrate on this work.

References
1.

SDGI. Sust. Dev. Goals Indicators, 20172.

SDGs. Sustainable Dev. Goals, 2015

3.

Berman, J., Principles of Big Data: Preparing,
Sharing & Analyzing Complex Inform.
Morgan Kaufmann, 2013

4.

Popper,K.,

Objective

Knowledge:

An

Evolutionary Approach,1972
5.

Dean, T., Kanazawa, K., A model for Reason.
About Persist. & Causat. Comput. Intel., 5, 2,
142-150, 1989

6.

Mwitondi, K., The Role of Comput. Intel. In
Dev. Countries; The First Seminar on Comput.
Intel. for Societal Dev. in Dev. Countries,
Sheffield, 17th February 20177.

Mwitondi, K., Big Data Challenges andOpportunities in the Dev. World. The 4thIntern. Conf. on Big Data Analy. & Data
Mining: Future Techn. for Knowledge Discov.
Data, 2017

69

for

E.,

Age-Period-Cohort Analysis of Data Obtained from
Repeated Social Surveys such as
the Surveys on the Japanese National Character
Takashi Nakamura1*
1* Center for Social Data Structuring, The Joint Support-Center for Data Science Research,
The Research Organization of Information and Systems (ROIS),
10-3 Midori-cho, Tachikawa, Tokyo, 190-0014, Japan
Email: nakamura@ism.ac.jp

Summary. Cohort analysis is a method of estimating the age, period and cohort effects from a set of
repeated social survey data. However, this technique confronts an identification problem. The author has
successfully overcome this problem by introducing a Bayesian model with a gradually changing parameterassumption and a procedure for selecting the optimal model based on the Akaike Bayesian information
criterion (ABIC). This keynote presents the Bayesian logit age-period-cohort model and its application to theJapanese national character study data.
Keywords. Repeated cross-sectional survey, Identification problem, Bayesian logit model, ABIC.

1. Introduction

2.2 Logit age-period-cohort model

Cohort analysis is a method of estimating the age,Let 𝜋𝑖𝑗 denote the response probability of acertain category of a question for the 𝑖 th age

period (calendar year), and cohort (birth time)group in the 𝑗 th survey period. The logit age-effects from a set of repeated social survey data,

period-cohort (APC) model decomposes the logit
𝜂𝑖𝑗 of 𝜋𝑖𝑗 intoclassified by age group and survey period. Thismethod is widely employed in various fields [1].
However, it confronts an identification problem,𝜂𝑖𝑗 ≡ log �

wherein the age, period, and cohort effects
cannot be uniquely decomposed without some𝜋𝑖𝑗
� = 𝛽 𝐺 + 𝛽𝑖𝐴 + 𝛽𝑗𝑃 + 𝛽𝑘𝐶 , (1)1 − 𝜋𝑖𝑗
𝑖 = 1, … , 𝐼; 𝑗 = 1, … , 𝐽,

where 𝛽 is the grand mean and 𝛽𝑖𝐴 , 𝛽𝑗𝑃 and𝐺

prior information. To overcome this problem, [24] introduced a Bayesian model with a gradually

𝛽𝑘𝐶 are the effects of the 𝑖 th age, the 𝑗th period andchanging parameter assumption and a procedure

the 𝑘th cohort, respectively. Further, 𝐼, 𝐽 and 𝐾 arefor selecting the optimal model based on thethe numbers of the age groups, survey periods

Akaike Bayesian information criterion (ABIC) [5].

andcohorts,

relationships,

2. Bayesian logit age-period-cohort

respectively.

Note

that

𝑘 = 𝑘(𝑖, 𝑗) = 𝑗 − 𝑖 + 𝐼

the

and

𝐾 = 𝐼 + 𝐽 − 1 hold for an 𝐼 × 𝐽 standard cohort
table.

model for a standard cohort table

Model (1) can be rewritten in terms of vectors

2.1 Standard cohort table

and matrices as

A table of observed proportions, classified by age

𝜼 = 𝛽 𝐺 𝟏 + 𝑿𝐴 𝜷𝐴 + 𝑿𝑃 𝜷𝑃 + 𝑿𝐶 𝜷𝐶 ,(2)where 𝑿𝐴 , 𝑿𝑃 and 𝑿𝐶 are appropriate designgroup and survey period, is called a standard
cohort table. In this table, the range (in years)

matrices that express the relationship between

covered by each age group equals the interval (inthe cells in a cohort table and the three respective

years) between successive survey periods. In suchfactors. The parameter vectors 𝜷𝐴 , 𝜷𝑃 and 𝜷𝐶 ina table, a cohort travels over the cells of the tablein the lower-right direction.

70

𝑀
1
1
𝜋(𝜹∗ |𝝈) = (2𝜋)− 2 |𝜮|−2 exp �− 𝜹′∗ 𝜮−1 𝜹∗ �,2
isobtained
from
Eq.
(4)
where
2
2
2
2
2
2 ),
and
𝜮 = diag(𝜎𝐴 , … , 𝜎𝐴 , 𝜎𝑃 , … , 𝜎𝑃 , 𝜎𝐶 , … , 𝜎𝐶

Eq. (2) are subject to the following zero-sum
constraints:
𝟏′ 𝜷𝐴 = 𝟏′ 𝜷𝑃 = 𝟏′ 𝜷𝐶 = 0.

(3)

2.3 Log likelihood function

𝑀 = 𝐼 + 𝐽 + 𝐾 − 3 . Now, model (2) can be

Let 𝑚𝑖𝑗 denote the sample size and 𝑦𝑖𝑗 therewritten as

observed response count for the (𝑖, 𝑗) cell.

𝜼 = 𝛿 𝐺 𝟏 + 𝑿𝐴 𝑫𝐼− 𝜹𝐴 + 𝑿𝑃 𝑫𝐽− 𝜹𝑃 + 𝑿𝐶 𝑫𝐾− 𝜹𝐶 .

Assuming a product binomial distribution for 𝒚,

2.6 ABIC and MAP estimate

the kernel of the log likelihood function of modelTo estimate the parameter vector 𝜹 = [𝛿 𝐺 , (𝜹∗ )′ ]′(2) with the constraints (3) is given bylog 𝑓(𝒚|𝜷) = 𝒚′ log 𝝅 + (𝒎 − 𝒚)′ log(𝟏 − 𝝅),where 𝜷 = [𝛽 𝐺 , (𝜷𝐴 )′ , (𝜷𝑃 )′ , (𝜷𝐶 )′ ]′ .

(or 𝜷), the hyperparameters are first determinedby minimising ABIC, which is considered to be afunction of 𝝈, defined by

2.4 Identification problem

ABIC(𝝈) = −2 log ∫ 𝑓(𝒚|𝜹) 𝜋(𝜹∗ |𝝈)d𝜹∗ + 2ℎ,
where ℎ is the number of free parameters. Once 𝝈
�is obtained, the maximisation problem
max log 𝑓(𝒚|𝜹) 𝜋(𝜹∗ |�𝝈)For an arbitrary real number ∆, let

Then,𝛾𝑖𝐴 = 𝛽𝑖𝐴 + {𝑖 − (𝐼 + 1)/2}∆,
𝛾𝑗𝑃 = 𝛽𝑗𝑃 − {𝑗 − (𝐽 + 1)/2}∆,
𝛾𝑘𝐶 = 𝛽𝑘𝐶 + {𝑘 − (𝐾 + 1)/2}∆.

𝜹can be solved to obtain the maximum a posteriori�.
(MAP) estimate 𝜹
Candidate models such as the G model with

𝛽 𝐺 + 𝛾𝑖𝐴 + 𝛾𝑗𝑃 + 𝛾𝑘𝐶 = 𝛽 𝐺 + 𝛽𝑖𝐴 + 𝛽𝑗𝑃 + 𝛽𝑘𝐶 = 𝜂𝑖𝑗 ,can be demonstrated, which shows that model

only the grand mean, A, P, C, AP, AC, PC, and APC(1) has an infinite number of decompositions thatmodels can be considered and compared. For

provide the same 𝜂’s. This is the identification

example, the C model is expressed by 𝜼 = 𝛽 𝐺 𝟏 +problem in cohort analysis.

𝑿𝐶 𝜷𝐶 .

Consider the sum of the squares of the first-

order differences of the effect parameters

3. Application

including
𝐼−1

𝐼−1

𝑖=1

𝑖=1

An application of the above mentioned Bayesian

𝐴 )2
𝐴 )2
�(𝛾𝑖𝐴 − 𝛾𝑖+1
= �(𝛽𝑖𝐴 − 𝛽𝑖+1
+ 2(𝛽1𝐴 − 𝛽𝐼𝐴 )∆ + (𝐼 − 1)∆2 ;cohort model to the Japanese national character

this suggests that minimising the weighted sum

study data is shown.

of these differences is key to attaining aparsimonious decomposition and overcoming

References

the identification problem.

2.5 Gradually

Changing

1.

Parameter

Assumption
To overcome the identification problem, anassumption,suggestedintheprevious

subsection,iswhereinsuccessive

made,

2.

parameters gradually change. This can be realised3.

by minimising the following sum of squares,
𝐼−1

𝐽−1

𝐾−1

1
1
1
2
𝐴 2
) + 2 ��𝛿𝑗𝑃 � + 2 �(𝛿𝑘𝐶 )2 , (4)2 �(𝛿𝑖
𝜎𝐴
𝜎𝑃
𝜎𝐶

𝑗=1
𝐴
𝐴
𝐴
where 𝛿𝑖 = 𝛽𝑖 − 𝛽𝑖+1
𝑖=1called hyperparameters.

𝑘=12
and 𝜎𝐴 , 𝜎𝑃2 and 𝜎𝐶2

4.

are

Letting the difference vectors including
𝜹 = 𝑫𝐼 𝜷𝐴 , 𝜹∗ = [(𝜹𝐴 )′ , (𝜹𝑃 )′ , (𝜹𝐶 )′ ]′ and thehyper-parameter vector 𝝈 = [𝜎𝐴2 , 𝜎𝑃2 , 𝜎𝐶2 ]′ , a prior

5.

𝐴

distribution,

71

Mason, M. M., Fienberg, S. E., (Eds.), Cohort
Analysis in Social Research, Beyond theIdentification Problem. Springer-Verlag, New
York, 1985
Nakamura, T., A Bayesian cohort model forstandard cohort table analysis. Proc. Inst.
Statist. Math., 29, 77-97, 1982 (in Japanese)
Nakamura, T., Bayesian cohort models forgeneral cohort table analyses. Annals Inst.
Statist. Math., 32, 353-370, 1986
Nakamura, T., Reconsideration of a Bayesian
age-period-cohort model with age-by-periodinteraction effects. Proc. Inst. Statist. Math., 53,
103-131, 2005 (in Japanese)
Akaike, H., Likelihood and the Bayes
Procedure. In: Bernardo, J. M., et al. (Eds.),
Bayesian Statistics, University Press, Valencia.
143-166, 1980

A Python library for parallelised particle filters
Shin’ya Nakano1*, Yuya Ariyoshi1, 2, and Tomoyuki Higuchi1
1* The Institute of Statistical Mathematics, ROIS, 10-3 Midori-cho, Tachikawa, 190-8562, Japan2
Now at Nippon Bunri University, 1727 Ichigi, Oita 870-0397, Japan
Email: shiny@ism.ac.jp

Summary. Particle filters are state-estimation techniques based on Monte Carlo approximation that use alarge number of particles. Particle filters are applicable even to nonlinear or non-Gaussian problems. They
are thus used for a variety of purposes. However, particle filter methods take a prohibitive amount ofcomputational time for high-dimensional problems when a huge number of particles required. Parallel
computing is an effective way to reduce the computational time, but the particle filtering algorithm is noteasy to parallelise. We are developing a Python library named P3 (Python library for parallelized particle filter),of the particle filtering algorithms with high parallel efficiency. In this paper, we explain an overview of thedesign and characteristics of P3.
Keywords. Particle filter, Sequential Bayesian estimation, Parallel computing.

1. Introduction

use parallel computation. However, attempts to

Particle-filters (PF) [1, 2] are state estimation

parallelise the standard filtering procedure usedin PF methods not only result in extremely

techniques applicable to non-linear and non-complicated programmes but also fail to achieve

Gaussian state-space models and they used for asignificant parallelisation efficiency. A number ofwide variety of purposes, including nonlinear

methods for improving the efficiency of paralleltimeseries analysis, tracking of targets throughimplementations have been proposed [3-5], butmoving images, and data assimilation. PF

the problem of programming complexity remains

techniques, which are based on the philosophy ofunresolved.

Monte Carlo methods, use large numbers of

The code that we present here, P3 (Python

particles to represent the probability distribution

libraryof state variables and perform calculations via
sequential Bayesian estimation. The accuracy

filter),islanguage, an abundance of powerful packages

particles used, so in PF methods, using the huge

arenumber of particles is desirable. In particular, asavailable,

includinghigh-performance

numerical programming libraries such as numpy

the number of state variables to be estimatedand scipy, libraries for computational statistics

simultaneously increases, the well-known “curse
becomesparticle

Although Python is an interpreted programming

represented increases with the number ofdimensionality”

parallelised

use of PF methods with high parallel efficiency.

with which the probability distribution is

of

for

implemented as a Python library to facilitate theand machine learning, and parallel computing

increasingly

tools such as mpi4py. Furthermore, the use ofprominent, and the number of particles needed

Python seems to have made significant inroads into represent the probability distribution grows

many fields of scientific and technical computing.

exponentially. Because the computational cost

Existing Python libraries implementing sequential

increases as the number of particles grows, theBayesian estimation methods applicable tocomputation time is a major issue in practical PF

general state-space models include filterpy [6].

implementations.

However, P3 was designed with the specific

One approach for achieving high-speed

intention of using parallel computation to handle

inference using large numbers of particles is to72

nonlinear, non-Gaussian state-space models ofare constructed from simulation models, therelatively large sizes.

variable names used in the original simulation
code are available, and it is convenient to use
3

2. Overview of P

these when writing code for system models as

P3 offers two types of methods to improve thewell. For this reason, P3 does not store statevectors as arrays but rather as instances of a classease of parallelising the resampling step. One is(structure) called class State. Detailed explanation

hierarchical resampling [4] and the other ison P3 is given in our recent paper [8].

alternately lattice-pattern switching (ALPS) [5, 7].
For linear state-space models, to which Kalman

Acknowledgments. The development of P3 wasfiltering is applicable, specifying matrices such assupported by a Grant-in-Aid for Scientific

the state transition matrix and observation matrix

Research (B) (project number 26280010) from thesuffices to define the model. However, for theJapan Society for the Promotion of Science.

general nonlinear state-space models handled by
PF methods, the way in which the problem isdefined varies from problem to problem. For

References

example, data assimilation problems make use of1.

partial differential equations of various forms, and
non-Gaussian probability distributions can be
involved. To ensure that P3 can be applied to a2.

wide variety of cases, the code is designed toallow users some amount of freedom in defining

3.

the state-space model. To allow the computationof these equations to be carried out by P3 code
modules, the user must describe state vectors

4.

and state-space models in predefined formats(class State and class System, respectively)
defined in system.py. The functions needed to
execute PF calculations are provided as member
functions of class Filter; once class State and class

5.

System have been defined, the user can invoke
functions in class Filter to solve state-estimation
problems.
Many existing computational libraries forsequential

Bayesianestimation

have

6.

used

implementations in which the state vector is

7.

organised in the form of a single array. However,the particle filter on distributed computing

in high-dimensional state-space models, it is not

system. in Proceedings of 15th International

uncommon for the various state variables to have

Conference on Information Fusion, 2480–2485,

different meanings. In such cases, the range of2012

possibilities addressable by a computational code8.

is enlarged by allowing state variables withnames.

In

particular,for

Nakano, S., Ariyoshi, Y., Higuchi, T., P3: Python
Parallelized Particle filter library. Proceedings

different meanings to be treated as variables ofdifferent

Gordon, N. J., Salmond, D. J., Smith, A. F. M.,
Novel approach to nonlinear/non-Gaussian
Bayesian state estimation. IEE Proceedings F,
140, 107–113, 1993
Kitagawa, G., Monte Carlo filter and smootherfor non-Gaussian nonlinear state spacemodels. J. Comp. Graph. Statist., 5, 1–25, 1996
Bolić, M., Djurić, P. M., Hong, S., Resamplingalgorithms and architectures for distributed
particle filters. IEEE Trans. Signal Process., 53,
2442–2450, 2005
Nakano, S., Population-based quasi-Bayesianalgorithm for high-dimensional sequential
problems and hierarchization of it for
distributed computing environments. inProceedings of 2010 IEEE World Congress on
Computational Intelligence, 2010
Nakano, S., Higuchi, T., A dynamic grouping
strategy for implementation of the particle
filter on a massively parallel computer. in
Proceedings of 13th International Conferenceon Information Fusion, 2010
Labbel, R., Kalman and Bayesian Filters in
Python, ( http://filterpy.readthedocs.io ), 2015
Nakano, S., Higuchi, T., Weight adjustment ofof the Institute of Statistical Mathematics,

data(underassimilation problems in which system models

73

review)

Data Treatment Strategies and Some Science Projectsof PANSY Radar
Koji Nishimura1, 2*
1* Polar Environment Data Science Center, Midoricho 10-3, Tachikawa, 1908518 Japan
2
National Institute of Polar Research, Midoricho 10-3, Tachikawa, 1908518 Japan
Email: knish@nipr.ac.jp

Summary. The Project of Antarctic Syowa Station MST/IS Radar (PANSY) has started full time full power
operation of the radar from 2015 in Syowa station (69S, 39E). In this workshop, we paresent a talk about ourcurrent strategies about data archive, transport and publication, and some related science projects.
Keywords. PANSY Radar, data transport, data archive, data publication.

1. Introduction

We conduct some science projects that takeadvantage of massive amount of data.
1. Multiple channel Imaging Observations
Using multiple subarrays as independent receivers, it is expected that we could obtain highresolution spatial image of the scatters from themesosphere caused by a several hypotheticalmechanisms.
2. Low Altitude Observations:
The radar has a lack of sensitivity at theboundary layer from the ground to 1,500 m
because of the blanking time for receiver
protection. We are planning to employ a small setof auxiliary antennas solely dedicated forreception to cover this height range. In order toobtain accurate estimate of wind velocities fromthe equipment, however, we need to solve acomplicated inversion problem using quite a lotof data real time.
3. 4D Turbulence Observations.
At present, estimating the strength of theatmospheric turbulence from echoes requires
some specific spatio-temporal model that has notbeen approved by observations. We havedeveloped a theory directly to observe the spacetime spectrum of the turbulence using massive
multi-beam multi-channel radar observations.

PANSY Radar [1] is capable of measuring theneutral atmosphere from 1.5 to about 100 km in
altitude, and scatter from the ionosphere. Syowa
Station, in which the radar is installed, is isolated
in data communication, which depends on ageostation-ary satellite channel, as well as inphysical access solely supported by a dedicatedicebreaker ship. Radar is a type of instrument that
potentially generates large amount of data andthe limitation of the data transport is a persistentissue to be dealt with to maximize the potentialof the instrument. In this presentation, we talkabout data management and transport strategies,
and science projects that depend on the use of
massive amount of data.

2. Data Transport & Archive
Our radar is continuously in operation andyielding data of roughly 3 MB/min (~4 GB/day) as
time series. In order to reduce the occupancy inthe communication bandwidth, however, we
reduce the time resolution of data to about 400
kB/min and transfer realtime via Intelsat to Japan.
The full resolution original data are manually
carried to Japan once a year by the ship. Until thetransport, for a risk of accidental losses, the data
are archive with the distributed storage system
specially developed for PANSY, which is installedin physically separate buildings in the station.

References
1.

3. Some Future Projects
74

Sato, K., Tsutsumi, M., Sato, T., Nakamura, T.,
Saito, A., Tomikawa, Y., Nishimura, K., Kohma,
M., Yamagishi, H., Yamanouchi, T., Program of
the Antarctic Syowa MST/IS radar (PANSY). J.
Atmospheric Sol.-Terr. Phys., 118, 2-15, 2014

Domestic and international activities of DOI-minting to solarterrestrial physics data and their citation in publication
Masahito Nosé 1*, Yasuhiro Murayama 2, Takenari, Kinoshita 3, Yukinobu Koyama 4,
Michi Nishioka 5, Mamoru Ishii 5, Manabu Kunitake 2, Koji Imai 2, Toshihiko Iyemori 6,
and Takashi Watanabe 2
1* Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
Strategic Program Produce Office, National Institute for Information Communications Technology, Tokyo,
Japan
3
Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan
4
Oita National College of Technology, Oita, Japan
5
World Data Center for Ionosphere and Space Weather, National Institute for Information Communications
Technology, Tokyo, Japan
6
World Data Center for Geomagnetism, Kyoto, Kyoto University, Kyoto, Japan
Email: nose.masahito@isee.nagoya-u.ac.jp
2

Summary. Data-DOI, data publication, and data citation will promote “Open Science”. Recognizing theirimportance, solar-terrestrial physics (STP) data centers in Japan have been working to mint DOI to their
database since August 2013. We participated from October 2014 in a 1-year pilot program for DOI-minting
to science data launched by Japan Link Center, which is one of the DOI registration agencies. In the pilot
program, a procedure of the DOI-minting for STP data was established. As a result of close collaboration withJapan Link Center, the first case of data-DOI in Japan (doi:10.17591/55838dbd6c0ad) was created in June
2015. The first case of data citation in Japan was also made. As of September 2018, there are 18 data-DOIs for
the STP data. The effort of the DOI-minting to scientific data will be continued for database in our data
centers and those in other fields of Earth Science.
Keywords. data DOI, data publication, data citation, World Data System, solar terrestrial physics.

1. Introduction

recognition and rewards for their labors to

The DOI system was originally developed bypublish and manage data set in the same way as
for traditional publications. Figure 1 explains apublishers and introduced as a common identifier

benefit of DOI-minting and data publication forfor publication in late 1990s. Now, more thandata providers. First, data providers publish their

5000 publishers participate in the DOI system.

data with DOI. This activity is called “data

DOI is applicable not only for usual publication

publication”. When researchers or data users use

articles but also for any objects such as a piece ofthe data in their researches and publish a paper,

online content (e.g., PDF files, movie files, etc.) or

the data can be cited in that journal papers witha physical asset (e.g., DVD, an item of equipment,

the DOI (i.e., “data citation”). Then, as usualrock samples, etc.). Therefore, it can be mint topublication, it becomes possible to count the

research data or database.

usage of data or to measure the importance ofMinting DOI to scientific database and data

the data, which is called “data citation metrics”.

citation with DOI provide much benefit to both

According to the results of metrics, the data

researchers and data providers. (1) Researcher

providers can gain “rewards” for their publishedcan more easily locate the data used in the paper,

data. This gives motivation or incentive of dataobtain necessary information of the data (i.e.,

publication to the data providers, resulting in

metadata), and validate the findings of the paper.
(2)

Data

providers

can

gain

formation of the positive cycle.

professional

75

Figure 2. DOI system and 10 registration agencies.
Japan Link Center (JaLC) is chosen for a registration
agency in our practice. We develpoed a sever thatworks solar-terrestrial physics data centers and JaLC tomint DOI to the solar-terrestiral physics database.

Figure 1. Benefit of DOI-minting for data providers.
Positive cycle is formed with data publication, data
citation, data citation metics, and rewards for data
publication.

2. DOI-minting

to

JaLC started a 1-year pilot program to mint
DOI to the database from October 2014. As aresult of close collaboration with JaLC, we

solar-terrestrial

successfully created the first case of data-DOI in

physics data

Japan (doi:10.17591/55838dbd6c0ad) in June

Recognizing the importance of data citation,2015 for mesospheric wind velocity data (30min.

solar-terrestrial physics data centers in Japanmean) observed with MF radar at Poker Flat,

started discussion to mint DOI to their own

Alaska. As of September 2018, there are 18 data-

database in August 2013. In DOI-minting, we

DOIs for the STP data in Japan.

need a contact to a relevant “Registration Agency”
which is qualified by International DOI foundation

3. Conclusions(https://www.doi.org/registration_agencies.html).

Since we have more data in our data center, we

There are currently 10 registration agencies, somewill continue this activity. We will also share ourof which are Crossref and DataCite. Eachexperience and system with others who are

registration agency has its own field of specialty.

interested in DOI-minting to their database.

After survey and discussion, we find that Japan

In the field of geophysics (including solar

Link Center (JaLC) is a proper agency to register

terrestrial physics), interests to the DOI-minting

DOIs for our case, because JaLC aims at public

are rapidly growing. At the next International

information services to promote science andUnion

technology in Japan and it handles scientific andGeodesy

andGeophysics

GeneralAssemblies that will be held in Montreal, Canada,academic metadata and content from holders

in July 2019, a joint session entitled “Geoscience

nationwide, including national institutes anddata

universities.

licensing,citation”

As shown in Figure 2, we develop a web-isproducing,planned.

This

publication
sessionandsolicits

contributions presenting actual practices andbased system to register metadata with JaLC andfuture plans of data licensing, producing,to create landing pages of data. This system ispublication, and citation of scientific data, andshared among the solar-terrestrial physics data

possible related topics. The international effort

centers. JaLC assigns the DOI prefix to each data

will be continued for DOI-minting to scientificcenter. The data centers submit metadata ofdata in solar terrestrial physics.

dataset, to which they want to mint DOI, in
addition to information of DOI suffix. Then theregistration server sends a query of mapping
between DOI and URL of the landing page to JaLC,
resulting in completion of the DOI-mining. The
system can handle version of the landing pages
when the data are updated.

76Importance of semantics and related challenges in scienceand humanities according to the Humboldtian ideal
Bernd Ritschel 1*
1* Graduate School of Science, Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo-ku, Kyoto, 606-8502, Japan (until 2016)
Email: berndritschel@yahoo.de

Summary. The Humbolditan education ideal was created by the brothers Alexander and Wilhelm from
Humboldt at the beginning of the 19th century. Both together formed the Prussian concept of a humanisticand holistic education and science system, combining both humanities and natural sciences.

The

philosopher Wilhelm emphasizes language skills, linguistics and semantics as main part of human and societal culture and therefore as a main pillar for furthering universal education and leading to a high level ofgeneral knowledge. His universal concept of humanistic education combines in an ideal way humanities,
natural sciences and art. Another pillar of his approach is academic freedom in education, science and research for students, teachers and scientists. Freedom especially means the independence from dogma, authority, tradition and politics. This also implies the non-interference and non-intervention of public and private funders with topics in education and science. Alexander and Wilhelm can be appreciated as the masterminds of a modern global Open Science approach, emphasizing a world-wide network of science andresearch, including a cross-domain inquiry and exchange of scientific data and results. While the success ofthese concepts in Germany had led many countries, such as USA and Japan to integrate Humboldtian ideas
into their education systems, the situation has shifted dramatically. Nowadays the trend of market-oriented
education and research in Germany as well as the influence of political correctness and gendermainstreaming is increasing, and is largely ignoring the Humboldtian humanistic education and science ideal. There is also a tendency that research funding increasingly comes from private entities and therefore research activities are especially influenced by private economic interests. The process of neglecting the
Humboldtian ideal is accompanied by the change of semantics and the reinerpretation of well known
concepts in science and society. Instead of a revival of Humboldt’s concepts, higher education systems andstandards are mainly driven by economic concerns and neoliberal ideas of the institutions. The new premisefor education, science and research, inevitably leads to a conflict between humanistic and market-driven
approaches to higher education. There are serious doubts about the long-term sustainability of this newmodel. The focusing on the market as main indicator for higher education, as well as scientific-technologicaland social progress, will narrow the human mind and intellectual power, and possibly even set back or stop
further societal developments. Acontemporary humanistic and ethics-driven Humboldtian approach should
also contain substantial education about recent and modern history. This knowledge is necessary for a comprehensive impact assessment of science and research activities for society and beyond, e.g. in nuclear sciences, but also technological developments, e.g. artificial intelligence and autonomous systems.
Keywords. Humbodlitan Ideal, Political Correctness, Gender Mainstreaming, Semantics, Concepts.

References
1.

Ritschel, B., The importance of the
Humboldtian ideal for education, science andresearch at present, http://wdc.kugi.kyotou.ac.jp/wdc/news/1603.pdf

77

Development of Metadata, Conversion and Archiving of theTime Series Data of the Completed Censuses and Surveys of the
BBS
Chandra Shekhar Roy1*
1* Senior Maintenance Engineer-IT, Bangladesh Bureau of Statistics, Statistics & Informatics Division,
Ministry of Planning, E27/A, Agargaon, Dhaka-1207, Bangladesh
Email: csroy.sme@bbs.gov.bd

Summary. Bangladesh Bureau of Statistics (BBS) has vowed to convert Census/Surveys micro data for next
generation technology format use processed in IBM proprietary technology. After Bangladesh’s
independence, there is a rich repository of statistical information in IBM 360 to ES/9000 mainframe 8600
tapes, dating back to late 1970s and early 2000s time. The overarching objective is to strengthen theprevailing national statistical archiving system. BBS will be making available of this large volume of
converted data to the citizens of Bangladesh (if needed world community) so that academic and scholarly
debates can take place taking cognizance of historical data. This is a big step towards dissemination of Big
Data covering Bangladesh’s economic developmental trend since its birth in 1971. By revisiting time series
data, it is hoped that well-informed and meticulous policies can be designed and formulated in the future.
Most fundamentally, availability and easy accessibility to such a large volume of Big Data will inspire
reassessing economic theories and indicators of development informing Bangladesh’s position in global
rankings like SDG.
Keywords. Mainframe, Historical data, IBM, Archiving, SDG.

1. Introduction

tool, faced serious criticism with theirmaintenance costs in Bangladesh. With the onsetof personal computers and a proliferation of
statistical software in the 1990s, the IBM ES/9000
was discontinued from 1999 onwards.
Fortunately a huge quantity of 9-track tape
was used to preserve the micro data. So there is arich repository of statistical information (census,
survey and geo-code) in these mainframe tapes,
dating back to late 1970s and early 2000s. The
Government of Bangladesh has initiated a project
to convert all the time series statistical microdata
set in a re-useable next generation technology
format. In this initiative, a legacy data recovery
lab will also be set up in the BBS.

The Bangladesh Bureau of Statistics (BBS) is thegovernment’s apex body providing technical andadministrative guidance in matters of all official
statistics in Bangladesh. BBS is the national
statistical organization (NSO) responsible forcollecting, compiling and disseminating statistical
data of all the sectors attributable to the
Bangladesh economy. The underlying function is
to meet data-needs of diverse users andstakeholders, e.g. national level planners,
development partners and other agencies.
Presently, the Bureau of Statistics possesses alarge volume of statistical information datingback to the time of Bangladesh’s independence
since 1971.
However, much of these data sets were storedon computer media that is no longer in common
use, deterring accessibility to such historical data.
In 1973, BBS installed the IBM System 360,
followed by IBM System 4341 in 1981 and IBM
ES/9000 a year later. Following emergence of
disk-system, these mainframe tapes, as a storage

2. Towards Big Data
The overarching objective of this step is tostrengthen the prevailing national statisticalarchiving system. The BBS will be making
available of this large volume of converted data
to the users in Bangladesh and for the worldcommunity, so that academic and scholarly78debates can take place for the cognizance ofhistorical data.
Data recovery and conversion frommainframe EBCDIC format into ASCII is a big step
towards dissemination of Big Data covering trendof economic development in Bangladesh since
her birth in 1971. By revisiting historical trends, it
is hoped that well-informed and meticulous
policies can be designed and formulated in the
future. Most fundamentally, availability and easy
accessibility to such a large volume of Data will
inspire revisiting economic arena and indicatorsof development informing Bangladesh’s position
in global rankings.

3. Outcome of the Project:-Supply older/historical data to the researcher’s,
stakeholders along with Metadata;-This information will be helpful to review thehistory of progress of the country;-Opportunities for sharing historical data both
local and foreign countries;-Legacy data set, will be helpful in big datapreparation;-Time series data, to predict future values basedon previously observed data.
Data Conversion project activities starts with
Planning, leading to Analysis and Design,
progressing to Conversion of Data, finishing in
Metadata - where converted data loaded in theTime series database.
In this stage BBS, in conjunction with IT expert &Statistician, prepares a plan, which is also
intended to shorten the duration of theConversion along with metadata process and also
reduces business impact and risk.

4. Execution of Data Conversion
In our dram project 60% percent works are
Machine intervention i.e. re-engineering where agroup of IBM mainframe compatible hardware( 9-track tape drive, Spool tape cleaner ) need to
be used. More than one scientific oven also needs
to be use to remove fungus and stickiness of 9track spool tapes.
i. Exclusive data conversion software needs to be
use to convert data from EBCDIC to ASCII format.
a) First of all this software will capture the datafrom the magnetic and will create an Image file,So that physical tapes no more needed in future.
b) Another beauty of this software is It will create
ASCII text from the captured image files. Finally,
we will get the numerals data i.e. 0 to 9.
Statistically we called it data records. All theserecords are meaningless unless we shape it withproper layout/dictionary.
ii. Rest 40% of our project work is Humanintervention. Within the 8600 tapes there is
programming tapes, data tapes and geo-code
tapes. Most of the programs were written under
COBOL language. In the programming and Geocode tapes the output come numerals withcharacters. There is a challenge if programming
tapes fails to read or convert corresponding datatapes, we need third party statisticians or SME
assistance. Defining and implementing data
quality standards to ensure consistency acrossthe different databases.
a) Their duty is to create metadata of the converted
ASCII file data. In this case, they need hard copy ofsurvey/census
data
collection
questionnaire/schedule. It can be found in the
publication of that particular census/survey.
b) Data Profiling and Cleansing: Ensure that proper
data profiling and data cleansing procedures arein place so that the original data is of high quality.
This helps to smoothen out the subsequent data
conversion procedures.
In light of data users view metadata should bevery constrictive and under a database also, we
called it Metabase (metadata + database) where
each and every instructions need to bementioned. Following data conversion, ensurethat the duplicate master data has eliminated,
reducing the risk of incorrect transactions andunreliable reports. The project should satisfy all
principles of data management and data
governance. The project adherence to FAIR
principles: The DCMPT philosophy has always
been consistent with the principles of Findability,
Accessibility, Interoperability, and Re-usability.
iii. The final goal of our project is to create ordevelop time series data both census and surveys
from year 1972 to 2021. Data conversion is thus, atask critical from both business and technicalperspectives.
Acknowledgments. The authors express theirappreciation to all collaborators of the project79

activities. They also acknowledge the members ofReferences

DCMPT and Metadata formulation committee for1.

their efforts to adhere to Historical data
conversion issues.

2.

80Open Government Data Strategy Bangladesh
Data for all, policy guidelines
John, W. C., Van Bogart, National Media
Laboratory, USAEstimation of age-specific reporting ratio of sentinel influenza
surveillance using seroprevalence data
Masaya M. Saito1*, Hiroshi Nishiura2
1

2*The Institute of Statistical Mathematics, Midori-cho 10-3, Tachikawa, Tokyo, 190-8562, Japan
Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-ku, Sapporo, 060-8638, Japan
Email: saitohm@ism.ac.jp

Summary. Using serosuveillance data during 2009/10 swine flu pandemic in Japan, we have assessed thewhat proportion of infected people are reported in the sentinel surveillance, which is open to public via
IDWR. After confirming these proportions do not change very much, we estimate incidence rate in seasons
2010/11 and 15/16. Our method, in principle, tries to count people who have been infected over the season
in concern regardless their severity, while conventional methods capture patients who have developed
enough symptoms so as to seek a medical institution. Therefore, the ratio of two may be interpreted as thecrude ratio between symptomatic and symptomatic plus asymptomatic. In average, the ratio is about three
and greater than unity even with the lowest estimates.
Keywords. Seasonal influenza, Disesase burden, Serosurveillance, Sentinel observation of influenza.

1. Introduction

seroprevalence. Therefore, the difference of the

In order to assess diseases burden, continuous

seroprevalence between 2010 and 2009 can beinterpreted as just the incidence rate during

monitoring of its cumulative incidence over year

season 2009/10.

is necessary. However, as for influenza, the cases

Mizumoto el al. [4] estimated age-specificare tracked only in sentinel medical institutions

incidence rates and concluded that the reported(SMIs), and we need to somehow estimate thecumulative incidence from such a sample.

proportion is much higher in children while veryA

low in elderly people.

conventional method, called multiplier method,

In this study, we try to

examine whether the estimated proportion

assumes SMIs would be sampled randomly from

reported is applicable to other season to estimate

all the institutes in Japan [1]. However, as theincidence rate by applying the same procedure to

proposers themself recognized, this assumption

successive seasons and examining whether

may not be justified since, in fact, SMIs arerecruited on a voluntary basis.

Under this assumption,

proportion reported is near constant.

In order toovercome this limitation, utilization of whole themedical claims linked to influenza patients [2] or

2. Methodreweighting each SMI w.r.t. the number of its

We use three data sets for (i) age-specific sentinel

outpatients at a certain duration [3] have been

influenza cases, (ii) serological prevalence, andconsidered.

and (iii) virological type-specific patients.

We consider to employ seroprevalence gain as

Assuming that sampling of serosurveillancean additional information. Serosurveillance isparticipants and eventually (via any clinical andheld every year before a major epidemic wave

statistical processes) recorded SMI cases arecomes (specifically July to September).

Seasongoverned by a single probability parameter each.

2009/10virus

The governing parameters are the infected

2009pdm and hence vaccination administrated inwasinducedbypandemic

incidence (denoted as q(a,v,y) with age-group a,2008 is expected to yield very limited gain invirological type v, and year y, or just q) and the81reported proportion as SMI cases (denoted as

4. Conclusions

p(a,v,y) or just p). As a summary, the likelihood

We have assessed what proportion of people whofunction is a product of three binomials:

have been infected in a single flu epidemic
season. Comparing such a assessment w.r.t thewhere M is Japanese population (in an age group),

2009 pandemic data against those w.r.t. 2010/11

m is the number of SMI cases, N1,N2 are theand 15/16 seasons, we conclude that this ratio isnumber of sero-surveillance participants in thealmost time invariant.

stnd

The incidence rate in

1 and 2 years, and n1,n2 are the corresponding

several seasons estimated using this fact will beseropositives.

introduced in the presentation.

The

goal

is

to

estimate

seroprevalence gain q2 − q1 and reported
proportion p. An estimate and an approximatedAcknowledgments.

confidence interval of each probability parameter

supported by JSPS KAKENHI Grant Number

are provided

18K11541.

by a conventional Gaussian

This

work

has

been

Disclaimer. This article is a memo-random on aapproximation: The estimates are given simply byq̂i = n/Ni (i = 1,2) and p̂i = m/{( q̂2 - q̂1)Mi}.

on-going research and any implication present
here is not authorized by the peer review process.

3. Results
In 2009/10 season, children and young adults are

References

intensively suffered from the pandemic flu: ages

1.

10-14 exhibited the maximal incidence, 62%(95%CI: 57-66), which is reported in Mizumoto etal. [4].

In seasons 2011/12 and 15/16, theincidence of this subtype took roughly constant2.

low values (~10%) over age groups, which iscomparable to other competitive strains.
The reported proportion assessed as the ratio
between expected cases from the seroprevalenceelevation andthesummarized in Fig 1.

sentinelobservationis3.

The estimate based on

2009pdm and AH3 in seasons 10/11 and 15/16
are identical to that based on the 2009 pandemic
within their uncertainties, though which are large.
Therefore the reported proportion estimated

4.

from the 2009 pandemic data is applicable toother seasons to assess the incidence rate.

Hashimoto, S., Murakami Y., Taniguchi K., etal., Annual incidence rate of infectious
diseases estimated from sentinel surveillancedata in Japan. J. Epidemiol., 13(3), 136-41,
2003
Nakamura, Y., Sugawara, T., Kawanohara, H.,
et al., Evaluation of estimated number of
influenza patients from national sentinel
surveillance using the national database ofelectronic medical claims. Jpn. J. Infect. Dis.,
68(1), 27-29, 2015
Kawado, M., Hashimoto, S., Ohta, A., et al.,Improvementof
Influenza
Incidence
Estimation Using Auxiliary Information in
Sentinel Surveillance in Japan. The OpenInfectious Diseases Journal, 10, 29-36, 2018
Mizumoto, K., Yamamoto, Y., Nishiura, H.,
Computational and Mathematical Methods in
Medicine. Article ID 637064, 1-8, 2013

Figure 1. Estimated reported proportion based on various subtypes and different seasons.

82

From Human Genome Project to Genome Cohort Study
Atsushi Shimizu1*
1* Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization
Iwate Medical University, 2-1-1, Nishitokuta, Yahaba-cho, Shiwa-gun, Iwate, 028-3694, Japan
Email: ashimizu@iwate-med.ac.jp

Summary. In 2003 when the Human Genome Project was completed, we finally obtained the blueprint ofourselves. At first, the function of genome sequence that accounted for 98% of the outside of the generegion was almost unknown. However, it was found that the human genome included several control
regions and non-coding genes by the huge improvement of molecular biology research and bioinformatics
analysis. Because of the advent of the next generation of the sequencer in the late 2000s, the personal
genome sequencing progressed rapidly and we became able to understand the humankind as a species bystudying individuals. Today, large-scale genome cohort studies are ongoing all over the world to investigate
the onset of diseases caused by the interaction between genes and environments. In this lecture, we will
present an overview ranging from the Human Genome Project to the genome cohort study.
Keywords. Human Genome Project, Genome Cohort Study, Bioinformatics.

83

Health - Prediction of Infectious Disease System Dynamics using
Machine Learning and Mathematics
Shailza Singh1*
1* National Centre for Cell Science, NCCS Complex, Ganeshkhind, SP Pune University Campus, Pune-411007, India
Email: singhs@nccs.res.in

Summary. In India, there is a global organization called as Data Science for India (DSI), dedicated toeducating and empowering high school students in India with a strong analytical foundation and critical
tools for data science. Targeted bootcamps workshops are held in different thematic areas concerning data
science viz., designing the DSI Data Literacy curriculum, creating the DSIx datascience bootcamp programand improving the DSI exploratory data science curriculum. India has seen more than 400% rise in demandfor data scientists, as per recent 2018 data index reports. Use of big data architecture is helping to manage
the expeditious data growth in health care industry. Here, I would like to discuss an empirical study
performed on an infectious disease, leishmaniasis through machine learning and synthetic biology.
Keywords. leishmaniasis, machine learning, systems biology, synthetic biology, genome.

1. Introduction

parameters. The credit of the systems wasmeasured using the accuracy, loss and error of
the systems. These integrated systems of theleishmaniasis dataset and neural network acted
to be the good choice for diagnosis with higher
accuracy and lower error. Through this approach,all records of the data were effectively
incorporated into the system. The experimental
results of mean square error and regression
analysis of genetic data, shows good prediction
accuracy and it could be a better solution for
diagnosing Leishmaniasis in future.
The model created through this approach forthe collected and processed data of human
Leishmaniasis genetic data has a good fit ofaccuracy for the diagnosing process. It showed
85.71% of accuracy, with just 1.94 as a value ofloss function. This makes it to be a good modelfor the prediction of Leishmaniasis using its
genetic dataset.

Leishmaniasis is an endemic parasitic disease,predominantly found in the poor locality of Africa,
Asia and Latin America. It is associated withmalnutrition, weak immune system of people andtheir housing locality. At present, it is diagnosedby microscopic identification, molecular andbiochemical characterisation or serum analysis forparasitic compounds. In this study, we suggest a
new approach for diagnosing Leishmaniasis using
cognitive computing and synthetic biology.

2. Machine Learning
Two different datasets of Leishmaniasis, Genetic
data and Image data, were collected fromdatabases and processed. The algorithm fortraining and developing a model, based on thedata were prepared and coded using python.
These algorithms and their corresponding
datasets were integrated using Tensor flow data
frame. An Artificial Neural Network trained modelwith multi-layer perceptron was developed as adiagnostic system for Leishmaniasis. It wasdeveloped using convolution neural network forimage data and recurrent neural network forgenetic data. The cognitive models of the trained
network were interpreted using the maps andmathematical formula of the influencing

3.
Systems
dynamics
Synthetic biology

through

Quality health care can be maintained bydescriptive, predictive and prescriptive big data
analytical techniques and systems approaches
being rightly called as predictive, preventive,
participative and personalized medicine, I, next84

talk about the work which revolves aroundmodulating signaling in Leishmania infected
macrophages. Till date, leishmaniasis is treatedwith traditional pharmaceutical approaches.
Newer approaches to modify the host action
against the parasite would be a better option toresolve the Leishmanial parasite. The strategies
adopted helped devise an immuno-modulatory
synthetic signaling circuit targeting CD14-TNF
and EGFR pathways in Leishmaniasis. Whenmacrophages are infected with Leishmania,
multiple signaling inputs arrive simultaneously
and/or sequentially, that are subsequently
integratedfor
ananti-inflammatory
pathophysiology
inleishmaniasis.
Novelbiological devices may be constructed, that revert
thesesignalsforapro-inflammatory
leishmanicidal response to curb infection. CD14,
TNF and EGFR pathways has been considered for
dynamical system theory analysis, whoseparameters were fitted within the known
biological limits for a desired and predictablesystem behavior (BIOMD0000000477). Network
metrices indicate that the signaling cascade is areal world network with crosstalk points MKK1/2,
MKK3/6, MKK4/7 and IKK–NFkB, central tomodulations for different phenotypic responses.
Furthermore, the downstream gene network of
CD14-TNF-EGFR pathway was reconstructed.
Network analysis showed that NFkB links thesignaling and gene network, used as a point ofintervention through a synthetic circuit
embedded within the negative autoregulatory
feedback loop. A chimeric protein kinase C (PKC)
was incorporated in the synthetic circuit, under
the transcriptional regulation of Lac repressor and
IPTG, as an inducer. The chimeric PKC via IKKb
phosphorylation activates NFkB, and modulates
the gene expression from an anti-inflammatory to
a pro-inflammatory phenotype in in vitro L. major
infected macrophage model.

In addition, the framework discussed here
provides a template for the inclusion of the newer
data that will be gathered in the coming years fordifferent types of infectious disease.

References1.

Mandlik, V., Shinde, S., Chaudhary, A., Singh,
S., Biological network modeling identifies IPCS
in Leishmania as a therapeutic target.
Integrative biology : quantitative biosciencesfrom nano to macro, 4, 1130-1142,
doi:10.1039/c2ib20037f, 2012
2 Mandlik, S. S. a. V., Structure based
investigation on the binding interaction oftransport proteins in leishmaniasis: insights
frommolecular
simulation.
Molecular
BioSystems, doi:10.1039/C4MB00713A, 2015
3 Orgnization, W. H., Global Health Observatory(GHO) data, 2016. Leishmaniasis : Situation andtrends, 2017
4 Mandlik, V., S. P., Bopanna, R., Basu, S., Singh, S.,
Biological Activity of Coumarin Derivatives as
Anti-Leishmanial Agents. PLOS ONE, 11, 1-15,
doi:10.1371/journal.pone.0164585, 2016
5 Milsee Mol, M. S. P., Singh, S., Immune signaltransduction in leishmaniasis from natural to
artificial system: Role of feedback loop
insertion. Biochimica et Biophysica Acta,
doi:10.1016/j.bbagen.2013.08.018, 2013
6 Milsee Mol, D. K. a. S. S., Nano-synthetic
devices in leishmaniasis: a bioinformatics
approach. Frontiers in Immunology, 6, 1-6,
doi:10.3389/fimmu.2015.00323, 2015
7 Prevention, U. C. f. D. C. a., Diagnosis ofLeishmaniasis. CDC’s Division of Parasitic
Diseases and Malaria, 2016
8 Alaa S. AlAgha, H. F., Bassam H. Hammo, Ala’ M.
Al-Zoubi, Identifying β-thalassemia carriers
using a data mining approach: The case of the
Gaza Strip, Palestine. Artificial Intelligence In
Medicine, doi:10.1016/j.artmed.2018.04.009,
2018
9 Shaikhina, T., N. A. K., Handling limited
datasets with neural networks in medical
applications: A small-data approach. Artificial
IntelligenceinMedicine,
75,
51-63,
doi:10.1016/j.artmed.2016.12.003, 201710 Khosla, E., D. R., Sharma, R. P., Nyakotey, S.,
RNNs-RT: Flood based Prediction of Human

4. Conclusion
How to tame big data with systems biology andsystems pharmacology is the order of the day.
Here, I have provided two examples as to how
this can be achieved, but in addition the stages
for many potential projects are set which can
explore different aspects of this intriguing puzzle.

85and Animal deaths in Bihar using Recurrent
Neural Networks and Regression Techniques.
Procedia - Computer Science, 132, 486–497,
2018
11 Quan Doa, T. C. S., Chaudri, J., Classification of
Asthma Severity and Medication Using
TensorFlow and Multilevel Databases.
Procedia - Computer Science, 113, 344–351,
2017
12 Pizzi, N., L.-P. C., Mansfield, J., Jackson, M.,
Halliday, W. C., et al., Neural networkclassification of infrared spectra of control and
Alzheimer’s
diseasedtissue.
Artificial
Intelligence In Medicine, 7, 67-79, 1995
13. Sacks, D., Noben-Trauth, N., The immunologyof susceptibility and resistance to Leishmania
major in mice. Nat. Rev. Immunol., 2, 845–858,
2002
14. Liese, J., Schleicher, U., Bogdan, C., The innate
immune response against Leishmania
parasites. Immunobiology, 213, 377–387, 2008

15. Ropert, C., Closel, M., Chaves, A. C., Gazzinelli,
R. T., Inhibition of a p38/stress-activatedprotein kinase-2-dependent phosphataserestores function of IL1 receptor-associate
kinase-1 and reverses Toll-like receptor 2- and4-dependent tolerance of macrophages. J.
Immunol., 171, 1456–1465, 2003
16. Little, J. W., The SOS regulation system. In
Regulation of Gene Expression in Escherichia
coli. in: (eds) Lin, E. C. C., Simon L. A., R.G.
Landes Company, Austin, TX, USA, 453 – 479,
1996
17. Rosenfeld, N., Elowitz, M. B, Alon, U., Negative
autoregulation speeds the response times oftranscription networks. Journal of molecularbiology, 323(5), 785-793, 2002
18. Becskei, A., Serrano, L., Engineering stability ingene networks by autoregulation. Nature,405(6786), 590-593, 2000

Git-Hub Link:
https://github.com/shailzasingh/Machine-Learningcode-for-analyzing-genetic-dataset-in-Leishmaniasis

86

The International Seismological Centre (ISC): 50-Year Miracle
Dmitry A. Storchak1*
1* International Seismological Centre, Pipers Lane, Thatcham, Berkshire, Rg19 4NS, U.K.
Email: Dmitry@isc.ac.uk

Summary. For over 50 years, the International Seismological Centre (ISC) has been fulfilling its unique
mission of producing the most definitive, complete and accurate long-term account of seismicity of the
Earth. This work is based on a free and open exchange of data with several hundreds of seismologicalinstitutions, networks and observatories in over hundred countries around the world. The result – theSeismological Bulletin of the ISC and several associated datasets are freely available to all and routinely usedby the Geoscience research community and referenced in several hundreds of scientific articles each year.
Keywords. Earthquake, bulletin, hypocentre, magnitude, mechanism.

1. Introduction

the earthquake related products that the ISC was

The International Seismological Centre (ISC) was

publishinghave

successfully

securedsuch

support for over half of a century.

founded in 1964 and follows the work of the

There is a clear realization that the long-term

International Seismological Summaries. It is based

operations conducted by the ISC could not bein the United Kingdom and charged withsustained on commercial principles due to a verycollection, integration and re-evaluation oflarge overall cost of running seismic networks

seismological bulletins of earthquakes and otherworldwide and routine processing of their data. It

seismic events produced by many hundreds ofis only by a mutual consent of mostly academic

geoscience institutions, seismic networks andand public institutions that the operations of theindividual observatories worldwide.

ISC could have been sustained for over 50 years.

2. The ISC Mission and Operating

3. Data Products of the ISC

Principles

In this presentation we describe the main data

The original data were always collected on a freeproducts of the ISC and their usefulness in various

data sharing principle, where each local data

fields of Geoscience research:

reporter was supplying a bulletin of earthquakes
and other seismic events in their region in
exchange for the global bulletin from the ISC that•

The ISC Bulletin (1964-2018)•

The

International

SeismographStationRegistry (1904-2018)

contains data of all reporters. In its work, the ISC
acts as a truly international organization, notbeing responsible to any particular government•

The IASPEI Reference Event List (1959-2014)•

The ISC-GEM Global Instrumental Earthquake
Catalogue (1904-2014)

and operating on a non-profit basis.
The operations of the ISC are only possible
due to long-term support of many tens ofMember-Institutions and commercial Sponsors.

•

The ISC-EHB Bulletin (1964-2015)•

The ISC Event Bibliography (1904-2018)•

The International Seismological Contacts
database.

There never was a solid commitment from any
Member to sustain the financial support of the
ISC for any considerable length of time.

4. Conclusions

Nevertheless, the quality and free availability of87Based on the widest possible long-term data

Acknowledgments. We are grateful for theexchange with seismic networks worldwide, thefinancial

ISC was able to produce the seismological

Institutions in 48 countries as well as 16 public

datasetstheand commercial Sponsors. Among them weadvancement of Geoscience. Free and open

acknowledge the NSF Award 1417970. We also

exchange of data has been a key to the ISC

acknowledge additional project support received

success.

from CTBTO, USGS (Award G18AP00035), FM

that

greatly

contributed

tosupportof65

current

Member-Global, Lighthill Risk Network, Willis Towers
Watson, BGR, Reftek, GeoSIG

88

and Guralp.

The ISC-GEM Global Earthquake Catalogue: Making Good Useof Historical Observations
Dmitry A. Storchak1* and Domenico Di Giacomo1
1* International Seismological Centre, Pipers Lane, Thatcham, Berkshire, Rg19 4NS, U.K.
Email: Dmitry@isc.ac.uk

Summary. The ISC-GEM Global Instrumental Earthquake Catalogue is a key product of the International
Seismological Centre (ISC). It is designed for use by the scientific community as well as by commercial
companies for evaluation of seismic hazard and risk on a regional and global scale. The catalogue covers theentire period of instrumental recordings in seismology and contains the parameters of moderate to large
earthquakes since 1904. It is important to note that major earthquake parameters in the catalogue(epicentre, depth and magnitude) were determined using the same procedure. Thus, usage of historical
paper-based global earthquake summaries and individual station bulletins was highly important to enlarge
the overall period for which the original seismogram measurements are available in digital form, ready forfurther interpretation.
Keywords. Global, earthquake, location, magnitude, homogeneity.

1. Introduction

available in digital form. Nevertheless gradual

The ISC-GEM Global Instrumental Earthquake

development of seismological observational
standards during the 20th century has led to a

Catalogue (last Version 5.1) is a key product of thegradualInternational Seismological Centre (ISC). It was
specifically designed and further developed as anstandards

Hence,

even

by
for

had to be recovered from various, often non-community.

digital sources.

The ISC-GEM catalogue was built using

For the years before 1964, the observational

funding contributed by both the ISC Membercommercial

thesesuch as amplitudes and periods of surface waves

needs of the seismic hazard and risk research

and

of

worldwide.

earthquakes since 1964, some of the key data,

extension of the general ISC Bulletin to satisfy theInstitutions

adoption

observatories

data were available mostly from paper-based

companies

sources that were only partly digitised at the startinterested in the development of this product.

of our project – the historical global earthquake

Much of the work on the early instrumentalsummaries (such as the ISS, ISA, Gutenberg

period (before 1964) has required collection,

Notepads etc) as well as a variety of individual

scanning and further processing of paper-based

seismologicaldocuments, some of which are available only in aobservatory

bulletinsthat

contained key information allowing to constrain

few copies distributed around the world. This

earthquake size.

international collaboration was key to the overall

A gigantic effort was made not only to bringsuccess of the catalogue.

these historical data into the digital era, but also
to process them and finally to make them

2. Working with historical documents

available to the entire community for alternative

The majority of the original routine observations

uses in other projects.

made by observatories around the world since
1964 have been captured by the standard
Seismological Bulletin of the ISC which is89

3. Working

withcommercial
4. Conclusions

companies

The

During the work on the ISC-GEM catalogue we

use for more accurate assessment of seismic

engineering. Several commercial companies werehazard and risk worldwide, they have also been

willing to provide limited funding for our work,

made available to a wider community for use inyet many chose to limit their support to mereother fields of research. We observed an expectedencouragement.

collision of the free data availability principle andOne of the major stumbling points was thethe size of the commercial funding for ouropen availability of the ISC-GEM catalogue at all

research. It was therefore very rewarding to seetimes, which was dictated by the majority of thearepublicfurther

Not only have these materials been put to goodearthquake insurance, re-insurance and seismic

that

and

a lot of work with historical paper-based materials.

both academic research and industries such as

Members

development

advancement of the ISC-GEM catalogue involved

received almost universal encouragement from

ISC

originalthat some commercial entities chose to support

institutions

our project despite no obvious commercial

themselves. Many commercial companies wereadvantages over their competitors.

unwilling to contribute funds towards thecatalogue development because of the product’s

Acknowledgments. We are grateful for the

open availability to those competitors who chosefinancial

not to contribute towards the project.

supportof65

current

Member-Nevertheless, it was possible to convince

Institutions in 48 countries as well as 16 public

some of the companies to contribute evenand commercial Sponsors. In particular, the ISCGEM work currently benefits from additional

though they had a very limited lead-time on

grants received from NSF, USGS, FM Global,

using the product ahead of their competitors.

Lighthill Risk Network, Willis Towers Watson and

Their support was often motivated by good will
and the general good benefits that the ISC-GEM

BGR, We specifically acknowledge the NSF Award

catalogue work brings to humankind.

1417970

90

and

USGS

Award

G18AP00035.

Research outline with regional theme: Examples using machinelearning, geographic information system, and social big data
Shizuo Suzuki 1*
1* Department of Electronic Control System Engineering, National Institute of Technology,
Numazu College, Ooka Numazu, Shizuoka 410-8501 Japan
Email: shizuo.suzuki@numazu-ct.ac.jp

Summary. Three examples of research with regional theme were presented in this workshop. An image
classification by aerial photographs was conducted to identify bamboo forests automatically. The two
representatively coastal forests dominated by black pine in Shizuoka Prefecture were investigated to showchanges in forest area during about 30 years. Main tourist hot spots around Mt. Fuji were analysed todemonstrate the potential of photo-sharing sites. These three results are considered to contribute forest
monitoring and maintenance, and mapping of tourism potential.
Keywords. Aerial photograph, black pine, Flick, Random Forests, World cultural heritage.

1. Introduction

2. A example using machine learning

National Institute of Technology, Numazu College

This research shows an image classification byis promoting regionally collaborative activities

aerial photographs to identify bamboo forests

among industry, academia and government with

automatically. The survey covered areas including

the aim of improving functions of education andthe foot of Mt. Ashitaka in Numazu City. Random

research, and revitalizing the regional economy.

Forests which is a type of machine learning wasStudents as well as teachers of the college

usedconduct joint research with staffs of companies,

classification accuracy was compared among RGB

municipalities, research institutes and universities(red-green-blue) color space, HSV (hue saturation

through graduation research and internship.

value) color space, and SLIC (simple linear

for

the

image

classification.

The

Three examples of research with regional

iterative clustering) processing. Additionally, thetheme were presented in this workshop. First, anclassification was conducted at canopy level ofexample using machine learning. Bamboo forests

bamboo forests. The model by the HSV color

were

aerialsystem combined with the texture classification

photographs and estimated by machine learning

showed the highest accuracy. These results

preliminary

investigatedbyin the eastern area of Shizuoka Prefecture. Next,

suggest that the proposed method is applicablean example using geographic information system.

to the forest monitoring and the planning as a

Changes in area of two coastal forests (one isuseful tool.

Miho-Matsubara which is a component of Fujisan

3. A

World Cultural Heritage site and the other isSenbon-Matsubara which is one of geo-site in Izu
Peninsula

UNESCO

GlobalGeopark)

exampleusing

geographic

information systemwere

Miho-no-Matsubara and Senbon-Matsubara areinvestigated by aerial photographs. Finally, an

the representatively coastal forests dominated byexample using social big data. Mapping of

black

tourism potential was conducted using social

pine

Prefecture.

data in Flickr around Mt. Fuji.

(PinusThis

thunbergii)

researchusedin

Shizuokatheaerialphotographs from 1974 to recent years toquantify the area change of coastal forest.

91Percentages of both black pine forest area

examine spatial patterns with areas of touristic

increased about 20% from 1976 to 1988 andvalue around Mt. Fuji. The spatial distribution

decrease about 11% to 16% from 1988 to recent

patterns were analyzed using spatial statisticalyear. These results suggest that the increasing

techniques in the GIS. The results revealed

and decreasing in coastal forest area are due todifferences in main hot spots between Japaneseenhanced growth of black pine itself as well as

and foreigner. Japanese posted photos taken ininvasion of broad-leaved tree species after

various places. In contrast, foreigners posted

seawall construction and logging after pine wilt

photos taken only in areas around the maindisease, respectively. These results are expected

tourist attractions on the north-eastern side of Mt.

to contribute to the future maintenance of theFuji. This study demonstrated the value of photo-

two coastal forests.

sharing site information as an approach to map
tourism potential.

4. A example using social big data
Social

big

data

show

great

promise5. The other examples presented byforpostergeographical research, especially in the field of
tourism geography. Visualizing the location

Preliminary investigations are presented byinformation of photographs taken by tourists is aposter session on 1) aesthetic value of cultural

promising method to measure tourist activity.

ecosystem services by mapping geotagged

Photo-sharing sites are important sources forphotos from social media data around Mt. Fuji

gathering location histories of tourists. Internet

and Izu Peninsula, 2) classification of Japanesephoto-sharing sites such as Flickr offer geotagged
photographs,

from

which

cedar forests with aerial photographs using

geographicalmachine learning approach, 3) tea leaf quality

information is retrievable by databases using theusing remote sensing techniques in Numazu city,

application programming interface (API) of suchand 4) results in Japanese historical charactersites. The aims of this study were to identify andrecognition by machine learning to locallyanalyze the main tourist hot spots around Mt. Fuji

historical documents.

and demonstrate the potential of photo-sharing
sites. Geotagged photographs on Flickr wereAcknowledgments. The three research examples

differentiated according to whether they had

were conducted in collaboration with Naoya

been taken by Japanese or foreigner. The study

Suwa, Koki Shimizu, Raiki Shida, Takahiro

used the API from Flickr to obtain the latitude andKatsumata, Kichi Kato，Ryodai Kato，Akari Sakai，longitude of the geo-tagged photographs, andYoshitaka Suzuki, Taisuke Yasuda, and Yusuke

then used geographic information system (GIS) to

Suzuki.

92

Activities of World Data Center for Geomagnetism, Kyoto
Satoshi Taguchi1,2*, Masahiko Takeda1, Hiroaki Toh1, Toshihiko Iyemori1
1* Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto
University, Kyoto, 606-8502, Japan2
Department of Geophysics, Graduate School of Science, Kyoto University, Kyoto, 606-8502, Japan
Email: taguchi@kugi.kyoto-u.ac.jp

Summary. The World Data Center (WDC) for Geomagnetism, Kyoto, provides geomagnetic field data
supplied from a worldwide network of magnetic observatories, and geomagnetic indices derived in this data
center to the community. In early 2011, we launched the near-real-time index data service. Immediately
afterwards, the number of requests to the data server doubled, and has reached as many as 2,000,000
requests per month since then. The launch of the near-real-time index service is the result of a successfulinternational collaborative project among six organizations in the USA, Russia, and Japan including this WDC
in the early 2000s. This practice, which has moved the research community towards open science, teaches usthat an effective approach for advancing WDC-based open science is to conduct collaborative projects
based on needs of users together with data providers and external organizations that recognize the value ofsatisfying user needs.
Keywords. World Data Center, near-real-time data service, collaborative project, open science.

1. Introduction

WDC data server is most often accessed by

The World Data Center (WDC) for Geomagnetism,
Kyoto, is a regular member of the International
Council for Science/World Data System. The
center provides geomagnetic field data supplied
from a worldwide network of magnetic
observatories and geomagnetic indices toresearchers specializing in solar terrestrial physics
and geomagnetism, graduate students majoringin these academic disciplines, and citizens with aninterest in those research fields.
The geomagnetic indices derived by the WDC
are the Auroral Electrojet (AE) index [1],Disturbance Storm-Time (Dst) index [2], and
ASY/SYM index [3]. These indices provide
information on how certain regions of the nearEarth space are electromagnetically disturbed,
which is often called Space Weather. The AE and
Dst indices have long histories, and both wereendorsed by the International Association of
Geomagnetism and Aeronomy in 1969.

domestic (Japan) servers, followed by those in
Russia, USA, and Germany.
Figure 2 shows the number of monthly
requests to the data server. The recent averagefrequency is approximately 2,000,000 times permonth. The frequency increased sharply in early
2011, which was exactly the time when the WDC
started providing near-real-time information on
the AE and Dst indices. i.e., the launch of the"Real-Time Space Weather Monitoring" system.
The launch of this system undoubtedly satisfied
the needs of users.

2. Users in the World
Figure 1 shows the percentage of the number of

Figure 1. Number of accesses to the WDC data server

accesses to the WDC data server by country. The

by country from January 2008 – December 2017.

93

that an effective approach for advancing WDCbased open science is to conduct collaborative
projects based on needs of users together withdata providers and external organizations thatrecognize the value of satisfying user needs,
which is summarized in Figure 3.

Figure 2. Monthly requests to the data server fromJanuary 1995–December 2017.

3. Near-Real-Time Index Service
3.1 International collaboration in early
2000s
The launch of the near-real-time index service istheresultofasuccessfulinternational

collaborative project between six organizations in
the USA, Russia, and Japan in the early 2000s forthe purpose of upgrading magnetometer andFigure 3. An effective approach for promoting WDCbased open science.

installing real-time data transmission instruments
in observatories in Siberia, Russia. Four out of thesixorganizations,

i.e.

the

Johns

Hopkins

University Applied Physics Laboratory (USA);

4. Conclusions

Geophysical Institute, University of Alaska (USA);The activities of the WDC for Geomagnetism,

NationalandKyoto have moved the research community

andtowards open science, and will do so for years toInstitute for Dynamics of Geosphere (Russia) arecome. An effective approach for advancing WDC-

neither a data center for geomagnetism, nor abased open science is to conduct collaborative

data provider (i.e. an organization operating an

projects based on the needs of users together

observatory) but are research institutes thatwith data providers and external organizations

recognize the value of satisfying user needs.

that recognize the value of satisfying user needs.

3.2 Open science accelerated by near-real-

References

InstituteCommunicationsofInformation

Technology(Japan);1.

time service
The near-real-time index service needs further
improvement. Users also want the WDC to launch
the digital information service for the near-real-

2.

time AE index, which is not yet included in thecurrent service. To provide this service, highly
advanced methods need to be introduced forprompt data-quality checks.
This

WDC

has

experienced

3.
successful

collaborations with other external organizations,which eventually moved the research community
toward open science. This practice teaches us

94

World Data Center for Geomagnetism, Kyoto,
Nose, M., Iyemori, T., Sugiura, M., Kamei, T.,
Geomagnetic AE index, doi:10.17593/15031–
54800, 2015
World Data Center for Geomagnetism, Kyoto,
Nose, M., Iyemori, T., Sugiura, M., Kamei, T.,
Geomagnetic Dst index, doi:10.17593/14515–
74000, 2015
Iyemori T., Rao, D. R. K., Decay of the Dst fieldof geomagnetic disturbance after substorm
onset and its implication to storm-substorm
relation. Ann. Geophys., 14, 608–618, doi:
10.1007/s00585-996-0608-3, 1996

Reframing Scholarly Communication by Persistent Identifier
Hideaki Takeda1*
1* National Institute of Informatics, 2-1-2, Hitotshubashi, Chiyoda-ku, Tokyo 101-8430, Japan
Email: takeda@nii.ac.jp
ORCID: 0000-0002-2909-7163

Summary. Open Science is a new form of science with the infosphere created by the Internet. Before theInternet era, scholarly communication is relatively well organized such as journal publications by scientists
and scholars in universities and research institutes. But there become more complicated and difficult withsuch open infosphere. Persistent Identifiers give filtering and anchoring scientific information among thevast infosphere. The system of persistent identifier has started by DOI, and now other PIDs are following DOI.
By assigning PIDs to every piece of scholar communication, scientific activities will be accessible, findable,and preservable.
Keywords. Open Science, Persistent Identifier, DOI, ORCID.

1. Introduction

usuallyWe, scientists and scholars, believe that science islanguages are quite different from ordinallanguages.

open. It simply means that there are no barriers

It is also going to be broken. The structure ofpeople, i.e., accessibility, understandability andprofessionality has been supported by social andunfortunately

economic system such as authority and budget.

digital technology, in particular, the Internet

The Internet flattened the social structure andtechnology is breaking these barriers step by step.

createdAt first, the barrier by accessibility is broken.
journals

systemsuch

asfocus on the first aspect, i.e., accessibility, because
it is now changing our business.

meetings. Except private meetings, they are open
in principle. But in fact, these books and journals

2. Digital Object Identifier (DOI)

are only available at academic libraries andcentral libraries so that ordinal people are not

STM publishing, in particular, scholar journals

easy to reach them.

are the important role in scholar communication.

The Internet technology, in particular, Web
createdeconomic

So now science is REALY going open. Here I

andcommunicated in public and private scholar

technology

newcrowdsourcing.

Before the Internet era, information on science is
and

now

are limited to participate into scientific activity.

There are invisible barriers between science and

books

is

scientists and scholars. Non professorial people

open to each other.

byIntelligencebecausereliability is ensured only in the professional

other. Science in different disciplines are notpublishedpeopleThe barriers by professionality means that

open to public, even science is not open to eachorArtificial

forknowledge like ontologies.

has not been true until now. In fact science is not

Fortunately

difficultsupporting people by their inference facility with

between science and people. But it is not true, or

professionality.

very

the

infosphere

Journals are media not only for communicating

where

but also delivering and archiving information.

everyone in everywhere can create, share and use

When the publishers were changing their journals

information. So can scientific information.

from paper to digital, they noticed that theThen the barrier by understandability is going
to be broken now.

Internet itself if not so suitable for delivering andInformation on science isarchiving information, because the Internet

95

mainly focused on the current information. But

We have usually trusted papers and books thatscientific information has longer life.

can be accessible via libraries. In the Internet Era,
infosphere is almost infinite, but we just use

Digital Object Identifier (DOI) was created topapers with DOI just like papers in libraries.

save this situation. DOI mechanism is very simple,
i.e., resolving DOI names to actual URLs where

So this anchoring function of DOI is important

information is available. While URLs tend tofor science to live with the infosphere by thechange rapidly, DOIs are fixed even original URLs

Internet. It is the role of Persistent Identifier (PID)

are changed. We can expect longer life of accessin science.

information just like catalogues in libraries.

4. The Information Flow in Science

DOI is not just a technical system rather it’svalue is that it is socially supported. The DOI

with PID

Foundation and the contracted registration

Now various PIDs are following DOI. ORCID andagencies (RA) keep the system work.

ISNI are identifiers to people. In particular, ORCID
is getting common for scientists and scholars

3. The Role of Persistent Identifiers

because many publishers use ORCID to identifier(PIDs) in Scholarly Communication

authors of papers. Open Funder Registry 1 is

The success of DOI tells us new insights about

started to identify funders supporting thescholarly communication in the Internet Era. The

research work. Identifiers for affiliations will start

infosphere is the promising space for scholarlysoon 2.

communication for its great flexibility. New types

We will have more IDs in science soon. Figure 1of communication can easily be created on theshows how information flow in science will beInternet. Sharing data and process is a goodwith PIDs. By assigning PIDs to every piece of

example. But the flexibility also causes thescholar communication, scientific activities will be

problems

accessible, findable, and preservable.

inscholarly

communication.

Information is easily lost. Judging reliability of
information is not easy. DOI and its metadata canFigure1: Information Flow in Science with PIDs
1
https://www.crossref.org/services/fundersuccessfully anchor the information on theregistry/
Internet with the traditional manner in science.
2
https://orcid.org/content/organization-identifierworking-group96

Useful Tools for Education and Capacity Building about Solar
Terrestrial Physics Study
Yoshimasa Tanaka1*,2,3, Norio Umemura4, Atsuki Shinbori4, Shuji Abe5,
Satoru UeNo6, Masahito Nose4, and IUGONET project team1

* Joint Support-Center for Data Science Research, Research Organization of Information and Systems, 10-3,
Midori-cho, Tachikawa-shi, Tokyo 190-8518, Japan
2
National Institute of Polar Research, ROIS, 10-3, Midori-cho, Tachikawa-shi, Tokyo 190-8518, Japan
3
The Graduate University for Advanced Studies (SOKENDAI), Shonan Village, Hayama, Kanagawa 240-0193
Japan
4
Institute for Space-Earth Environmental Research, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 4648601 Japan
5
International Center for Space Weather Science and Education, Kyushu University, Motooka, Nishi-Ku,
Fukuoka 819-0395, Japan
6
Kwasan and Hida Observatories, Graduate School of Science, Kyoto University, Kurabashira, Kamitakara-cho,
Takayama, Gifu 506-1314, Japan
Email: ytanaka@nipr.ac.jp
Summary. We introduce two tools for solar terrestrial physics study, developed by the Inter-university Upper
atmosphere Global Observation NETwork (IUGONET) project. One is a data analysis software based on Space
Physics Environment Data Analysis Software (SPEDAS); we have provided a plugin software for SPEDAS,
called iUgonet Data Analysis Software (UDAS), which enables users to deal with various upper atmospheric
data obtained by the IUGONET members using SPEDAS. The other is a metadata database for upper
atmospheric data, called IUGONET Type-A, which is one-stop web service that allows users to search data,
get the information of data, view quick-look plots of data, find scientifically interesting events, interactively
plot data, and proceed to more advanced data analysis using SPEDAS. These tools may also be useful for theeducation about solar terrestrial physics study in universities or high schools and capacity building inemerging countries.
Keywords. IUGONET, SPEDAS, analysis software, metadata database, upper atmosphere.

1. Introduction
Inter-university
Observation

allows users to cross-search various kinds of theUpperatmosphere

Network

upper atmospheric data distributed across the

GlobalIUGONET members, and the other is a data

(IUGONET;

analysis software that is capable of analyzing such

http://www.iugonet.org) is a Japanese inter-a variety of data in an integrated fashion. In thisuniversity project, which aims at sharing upper

paper, we present the characteristics of these tools.

atmospheric data, including solar and planetarydata,obtained

instruments

by

with

variousJapaneseground-based

universities

2. IUGONET Data Analysis Software

andinstitutes since International Geophysical Year

The iUgonet Data Analysis Software (UDAS)(IGY; 1957-1958). The project started in FY2009

developed by the IUGONET project is a plug-in

originally by Tohoku University, Nagoya University,

software for Space Physics Environment Data

Kyoto University, Kyushu University, and National

Analysis

Institute of Polar Research, and then some other

http://spedas.org/wiki/) [2]. The SPEDAS is a grass-universities and institutes joined the project. We

roots data analysis software for space physics

have primarily developed two tools; one is acommunity, which is written in the Interactive

metadata database, called IUGONET Type-A, which

Data

97

SoftwareLanguage(SPEDAS;(IDL;https://www.harrisgeospatial.com/SoftwareTechS

data. The IUGONET Type-A was produced mainly

oftwa/IDL.aspx). One of the important features offor researchers in the field of the solar terrestrial

SPEDAS is an automatic downloading of data files

physics, but it may be useful for the capacity

via the internet. The SPEDAS was originallybuilding in the emerging countries and thedeveloped as TDAS by scientists and programmers

education in universities or high schools. Weof the UC Berkeley and UCLA and otherregularly have data analysis workshops several

contributors to analyze satellite- and ground-

times a year in Japan and sometimes in otherbased observational data obtained by the THEMIS

countries, especially in Asia and African region, tomission [3], and then it was extended to support

diffuse the IUGONET data and tools.

various kinds of data from multiple missions. By
using the UDAS plugin, users can easily load,

4. Conclusions

visualize, and analyze the data released by the

The IUGONET project has developed the metadata

IUGONET members with useful functions in the

database and the integrated data analysis

SPEDAS/IDL.

software for the upper atmospheric data. It helps

Recently, we developed template routines thatto share the data with researchers in the solar

allow users to easily create routines for loading

terrestrial physics community, effectively analyze

their own data onto SPEDAS, by modifying only

variousabout 10 lines of the templates. At present, the

kinds

of

data,

and

promoteinterdisciplinary study. These tools may also be

templates called UDAS EGG (Easy Guide touseful for the capacity building in the emerging

Generate your load routines) support data files incountries and the education in the universities or

the Common Data Format (CDF) and the ascii

high schools.

format, and will support other formats in near
future, e.g., netCDF and FITS. This tool will help toAcknowledgments. The IUGONET project was

expand the base of potential users of SPEDAS.

supported by the Special Educational Research
Budget (Research Promotion) [FY2009] and the

3. IUGONET Metadata Database

Special Budget (Project) [FY2010-2014] from theSince the upper atmospheric data obtained by the

Ministry of Education, Culture, Sports, Science andTechnology (MEXT), Japan.

IUGONET members have been archived andopened to public separately by each university or
institute, it is often difficult and time-consuming

References

for users to find, get, and analyze the data. In order1.

to solve this problem, we have developed ametadata database for upper atmospheric data,
which can search various kinds of data distributed
across the IUGONET members [4]. The second
version of the metadata database was named
IUGONET Type-A (http://search.iugonet.org/). It
provides one-stop web service, which allows users

2.

to search data, get the information of data (i.e.,
metadata such as contact persons, access URL, anddata use policy), view quick-look plots of data, find3.

scientifically interesting events, interactively plot
data, and proceed to more advanced data analysis
using SPEDAS. The IUGONET metadata format was
designed based on the Space Physics Archive
Search and Extract (SPASE) metadata model withsome modifications for the upper atmospheric98

Tanaka, Y.-M., Shinbori, A., Hori, T., Koyama, Y.,
Abe, S., Umemura, N., Sato, Y., Yagi, M., Ueno,
S., Yatagai, A., Ogawa, Y., Miyoshi, Y., Analysis
software for upper atmospheric data
developed by the IUGONET project and its
application to polar science. Adv. Polar Sci., 24,
231-240, doi: 10.3724/SP.J.1085.2013.00231,
2013
Angelopoulos, V., The THEMIS mission. Space
Sci. Rev., 141(1-4), 5-34, doi: 10.1007/s11214008-9336-1, 2008
Abe, S., Umemura, N., Koyama, Y., Tanaka, Y.M., Yagi, M., Yatagai, A., Shinbori, A., Ueno, S.,
Sato, Y., Kaneda, N., Progress of the IUGONET
system - metadata database for upper
atmosphere ground-based observation data.
Earth, Planets and Space, 66, doi:10.1186/18805981-66-133, 2014

Research Data Management in Industry-Academia
Collaboration
Yuko Toda1*, Hodaka Nakanishi2
1* Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8605, Japan
Email: y.toda@med.teikyo-u.ac.jp
2
Teikyo University, 2-11-1 Kaga, Itabashi-ku, Tokyo 173-8605, Japan
Email: nakanishi@med.teikyo-u.ac.jp

Summary. The data management is an important issue, but it is not well discussed when contracting
collaborative research between industry and academia. Universities are recently requested to preserve
research data because of the research integrity while companies tend to hold their right to the data. The
description concerning research data management in the contracts on industry-academia collaboration of
Teikyo University was analysed. In the contracts of collaborative research, only a few contracts prescribe data
management issue. It is necessary to negotiate not only for research integrity, but also for the importance ofopen data and the social role of the universities considering the interests of companies. Technology transfer
offices of university should consider research data management in addition to intellectual property.
Keywords. collaborative research, contract, industry-academia, attribution, research data.

1. Introduction

attribution of data, the treatment of the research

The handling of research results, including

data in a contract depends on the definition of"outcome" [1].

research data as well as intellectual property, is an

Regarding the handling of data among

important issue in collaborative research between

companies, the Ministry of Economy, Trade andindustry and academia. Although the issues on

Industry has formulated "contract guidelines on

intellectual property is often discussed, thedata usage authority" in 2017 [2]. This guideline ismanagement of research data, such as creating,organized from the viewpoint of usage rights ofaccessing, using, storing, and disposing data, isresearch data. However, companies often ask for

not well discussed at the negotiations of contract

ownership of research data in the collaborative

between company and university.

research especially in a clinical research.

In this paper, the cases of research data

In the context of research integrity, it ismanagement in joint research contracts between

required to keep experiment notes, documents,

Teikyo University and companies are summarized.

numerical data, image data etc. for 10 years after

Several points to be considered for the researchdata management in the contracts are proposed.

the publication of the related papers [3]. To meet

2. Backgroundin the university properly while companies often

the requirement, research data should be stored
request the ownership of the data. It is necessary

For the treatment of intellectual property right,

to coordinate which party should keep thethe Ministry of Education, Culture, Sports, Science

research data in the collaborative research

and Technology formulates Sakura Tool which

between industry and academia.

indicates eleven types of contracts from theviewpoint of the attribution of intellectual

3. Research Data in Contracts

property rights in collaborative research. As the

In this study, 45 contracts of collaborative

Sakura tool does not explicitly include theresearch99

betweenTeikyoUniversity

andcompanies (excluding cancelled cases) which

For the coordination of industry and academia

were checked by Teikyo Technology Transfer

relationship, not only the importance of open

Center from April 2018 to August 2018 weredata and the social role of the universities, but

analysed. 29% of the contracts mentioned “data”

also research integrity should be considered andand 71% did not (Table 1). Here, the phrasethe"mention data" means that the term "data" isTechnology transfer offices of university should

described in the context of data management or

now consider research data management in

explaining research content, and it does not

addition to intellectual property.

interestsofthecompanies

as

well.

mean the mere use of the term such as for

References

explaining misconduct activities.

1.

Table 1. Description of “data” in contracts.
Description of data
“data” is described in the contract“data” is not described in the contract
Research results don’t include data
Research results may include data
Total

number (%)
13 (29%)32 (71%)
13 <41%>
19 <59%>45 (100%)

2.

3.

71% of total contracts did not mention data
management. About 60% of the contracts
without data description determine that alltechnological outcomes are considered to beresearch results and research data can therefore
be included in the research results. However,about 40% of the contracts limit research results
to materials, and research data is therefore out of
the scope.
29% of total contracts mentioned data anddetermined data management such as attributionof data or permission of using the data.
As data has a significant role in a clinicalresearch,

pharmaceuticalcompanies

often

request universities to make the data attribution
to the company in a contract. In the past, Teikyo
university allowed companies to hold theownership of the data, but now it is necessary foruniversities to have the right to access theresearch data for 10 years from the viewpoint ofresearch integrity.

4. Conclusions
In the contracts of the collaborative research
betweenindustry

management

of

and

researchacademia,
data

is

the

getting

important.

100

Anderson Mori & Tomotsune, Survey on theway of handling outcomes in collaborative
research in the open and closed strategy era,2018 (in Japanese)
Ministry of Economy, Trade and Industry.
Contract Guidelines on Data Utilization Right,
2017 (in Japanese)
Science Council of Japan, Answer: Improving
Integrity in Scientific Research, 2015 (inJapanese)Application of deep learning and large scale simulation to the
Earth science
Seiji Tsuboi1*, Daisuke Sugiyama1
1* JAMSTEC, Yokohama, Kanagawa, 236-0001, Japan
Email: tsuboi@jamstec.go.jp

Summary. Recent progress in both data science and large scale simulation is now giving an impact on Earthscience research. Particularly, application of artificial intelligence, which is realized by using convolutional
neural network, to the Earth science has shown possibilities to make significant progress in various fields.
Here we show a combination of deep learning approach and large scale simulation to develop newprocedure to locate earthquake. We have applied our procedure to regional earthquakes in Kanto region,
Japan. Although the number of earthquakes is limited, our results suggest that our approach may be
applicable to locate earthquakes in various scales.
Keywords. Deep learning, Seismology, Synthetic seismograms, large scale simulation, Spectral-Element
method.

1. Introductionof using this approach to locate earthquakes anddetermine

Traditional method of locating earthquake has
been based on the arrival times of seismic waves,read arrival times and amplitude of seismic waves

seismograms and deep machine learning. We

to locate earthquakes; three dimensional Earthcalculate theoretical seismograms for realistic

structure can be included without additional

three-dimensional Earth model by using thecomputational

Spectral-Element method program SPECFEM3D

tolocateearthquakes;

earthquakes which happen concurrently inobe) and use these seismograms to create seismic

different location, can be included in learning

wave propagation images at the surface of thedataset. This technique may be applied to

Earth. Then we use these images as training

determine moment tensor solution of thedataset of convolutional neural network. We

earthquakes in realtime monitoring of seismicbuild neural networks for determination ofactivity.

hypocentral parameters by using the package
TensorFlow (https://github.com/tensorflow), such

Acknowledgments.

as epicenter, depth, origin time and magnitude,
to

cost

seismologically rare but inevitable case, such as(https://github.com/geodynamics/specfem3d_gl

networks

ofof deep machine learning; there is no need to

combining numerically computed theoreticaltheseaccuracy

earthquake location and size as learning dataset

Earth model. Here we develop different approach

appliedare;accumulating theoretical seismograms for various

such as P-wave and S-wave, and one-dimensionalandmagnitude

hypocenter parameters can be increased by

We

used

the

seismic

waveform provided by the NIED K-net andactualearthquake

seismograms to examine if this procedure works

catalog

provided

Agency.

byJapan

to locate earthquake and determine magnitude.

MeteorologicalComputationof

Although the number of earthquakes is small andtheoretical seismograms was conducted on the
Earth Simulator and supercomputers in JAMSTEC,

the regional extent is quite limited, the results

Japan.

demonstrate that it is feasible to locate
earthquakes by using this approach. Advantages

101

Resource and Environment Scientific Data Sharing and Disaster
Risk Reduction Knowledge Service
Juanle Wang1*
1* Laboratory of Resources and Environment Information System, Institute of Geographic Sciences andNatural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing, 100101,
China
Email: wangjl@igsnrr.ac.cn
Summary. The disaster risk reduction is an urgent task facing the world. Many developing countries are
weak on the disaster risk reduction information processing and application capacity. Knowledge service
plays important role on disaster risk reduction field in the Big Data era. A Disaster Risk Reduction Knowledge
Service System (http://drr.ikcest.org) was built based on disaster information sharing and thematic
knowledge service, which realizes the sharing of knowledge resources such as disaster data, map, expert,
organization, document and courseware. In future, the Disaster Risk Reduction Knowledge Service should
focus on the standard for disaster metadata, the integration of disaster metadata resources, developing
disaster information search engine based on big data and location based disaster risk early warningknowledge service. The current global data integration and sharing trends and activities of WDS data centres
in China were also introduced in this presentation.
Keywords. Resource and Environment, Data Sharing, Disaster risk reduction, disaster data, knowledge
Service.

1. Introduction

regional disaster databases in a greater number

Disaster data is a necessity for establishingof developing countries. In 2015, UNESCO
proposed

Disaster Risk Reduction (DRR) capabilities. The
integration and sharing of DRR data and the

managedrepresent a fundamental transformation offrom‘possession’

to

and

and Cultural Organization (UNESCO) has always

thethematic

internationalbyknowledge
organisations,servicesforgovernment

institutions, research and educational institutions,promoting DRR knowledge sharing services[3].
ofofprovide a long-term source of DRR information

The United Nations Educational, Scientificimplementation

Academyflood disasters. This knowledge service will

the near future.

theChineseservice applications for earthquake, drought anddeeply influence the growth of human society in

With

the

around the world, especially on knowledge

knowledge services , which are expected to

DRR

by

established to address the demand of DRR works

[2]

in

the

collaboration, the DRR knowledge service wasto drive the continuation and development ofcooperationfortowards data integration and international

theories, methods and techniques are necessary

globalrequirements

Engineering (CAE). Based on current trends‘application'. Innovations in knowledge service

prioritisedof

Sciences and Technology (IKCEST), which istrend in the field of DRR[1]. Knowledge services
resources

setInternational Knowledge Centre for Engineering

provision of knowledge services is an emerging

knowledgeaconstruction of a DRR knowledge service by thebusinesses and broader society.

Sendai[4]

Framework for Disaster Risk Reduction , UNESCO
hopes to strengthen cooperation in disaster data
standards, DRR education, and both national and102

2. Resource and environment datadisasters,

includingearthquakes,

droughts,

flooding, and freezing rain and snow, and it

sharing trends
Resource and Environmental Scienceenables interactive application by users throughis athe online system.

comprehensive discipline that applies rationaluse of resources and environmental protection to

4. Thethe fields of production and environmental

RealisationofThematic

Knowledge Applications

construction from an ecological point of view.
The trends of global resource and environment

The use of thematic knowledge services is andata sharing are summarized as below.

important approach for the organisation of(1) Scientific Data Curation achieve a highservices in DRR knowledge service platform.

degree of consensus.

Based on the needs of its target users and(2) Scientific data and related research enterfeatured resources, a knowledge service system

the era of big data.
(3)

Scientificconstructs specialised service products by meansdatapublishingmodelof online and offline models, to provide valuablepromotes scientific data sharing.

DRR knowledge services to users around the(4) Data archiving policies are developed andglobe.

used in funding projects.

regarding(5) Authoritative data centers gather global

theknowledge

platform,

services

technology,

data,disaster prevention and mitigation. The modes of(6) Data center management models areservice include the query, browsing, downloading,designed and applied in varies data centers.
Manyresearchdata

centers

analysis, and visualization of various types ofaredisaster-related knowledge and resources. The

established in the world.

service contents cover eight aspects in three main(8) Scientific data sharing standards andcategories: resource content (including datamechanisms are needed.
(9)

provideseducation, and other aspects of current global

resources.

(7)

DRR

The

services, map services, institution services, expert

authenticationoftrustworthy

database services, and disaster event services),

repositories grows recently.

resource

dissemination

courseware

training(including

services

and

video
science

3. Technical Methodologies

popularization

The overall method of building the Disaster Risk

knowledge applications (e.g., Global Earthquake

resource

andInternational Experience in Natural Disaster Relief,the standard formulation for disaster-related

Map Visualization Services of China Historical

metadata as the breakthrough point to realise theknowledge

andDaily Distribution Map Service, China andReduction Knowledge Service System is to takecollection of various

services),

Disasters, etc.).

resources,

including a metadata-based disaster science
database, disaster map resources, a disaster5. Conclusions

expert database, a disaster institution database, a

Under the DRR directives of UNESCO, we havedisaster event database, a database of disaster

constructed a DRR knowledge service system foropen directory projects, a database of disaster-

the sharing of disaster information and therelated information Web mining, a disasterprovision of thematic knowledge services, based

literature database, a disaster popular scienceondatabase, and a disaster video courseware

standards. This system has successfully actualized

database. In the development environment of an

the integration and sharing of knowledge

open platform, the system delivers visualized

resources like disaster data, disaster maps, expert

subject-specific knowledge services in typical

103

the

formulationofdisaster

metadata

opinions, institutional data, research literature,

Geography and Agroecology, Chinese Academy

and educational materials.

of Sciences.

Disaster Risk Reduction Knowledge Service
system

(DRR)

can

be

accessed

References

online(http://drr.ikcest.org). Till the end of June, 2018,1.

the DRR platform has abstracted 12 thousand
page views per month, about 24% from abroad
and 76% from domestic. Targets for UN
Sustainable Development Goals and Sendai
Framework for Disaster Risk Reduction, DRR will2.

long-term persistently provide disaster risk
reduction knowledge services to internationalorganizations, government agencies, research
institutions, educational institutions, commercial
institutions and the public.
Acknowledgments.

Many

3.
Thanksfortheguidance by the experts of the Natural Science
Sector of the UNESCO Office for Disaster Risk

4.

Reduction and the Secretariat of the International
Knowledge Centre for Engineering Sciences andTechnology (IKCEST) of the Chinese Academy ofEngineering. Thanks all the team members from
Institute of Geographic Sciences and Natural
Resources Research, and Northeast Institute of

104

Xie, Y. B., Design Knowledge Services via
Internet-An Analysis on the Functions ofChina Knowledge Center for Engineering
Sciences and Technology (CKCEST). China
Mechanical Engineering, 28(6), 631-641, 2017
(in Chinese)Wen, Y. K., Jiao, Y. Y., Constructing a scientificframework for knowledge service in semantic
Web environment. Journal of Information
Resources Management, 1, 99-104, 2011 (inChinese)UNESCO
Disaster
Risk
Reduction,
http://www.unesco.org/new/en/naturalsciences/special-themes/disaster-riskreduction/
United Nations. Sendai Framework for
Disaster Risk Reduction 2015-2030. United
Nations Office for Disaster Risk Reduction(UNISDR),https://www.preventionweb.net/files/43291_
sendaiframeworkfordrren.pdf,
2015Multivariate analysis of the occupations of rental rooms byusing the housing information website data
Hayafumi Watanabe1*, Yu Ichifuji2, Masahito Suzuki3, Satoshi Yamashita41*Research Organization of Information and Systems, 10-3, Midori-ku Tachikawa-shi, Tokyo, 453-424, Japan
2
Nagasaki University, 1-14, Bunkyo-machi, Nagasaki City, Nagasaki, 852-8521
3
UD Asset Valuation Co., Ltd., 1-3-6, Andoujimachi,, Chuo-ku, Osaka 542-0061, Japan
4
The institute of statistical mathematics, 10-3, Midori-ku Tachikawa-shi, Tokyo, 453-424, Japan
Email: hayafumi.watanabe@gmail.com

Summary. The apartment loan is a loan for rentals such as condos and apartments. This loan is a very large
loan which is the account for a percentage of more than 10 percent of the whole Japanese banks' loan.
However, a risk model of the apartment loan with the appropriate accuracy has not been provided in Japan
mainly due to the lack of data.

Thus, in order to develop the risk model, we preliminarily analyse andcompare two types of data set: the survey data which is made by the real estate appraiser and the housing
information website data. As a result, it was found that (i)the web data is approximately corresponding to
the survey data which was made by experts with respect to statistical properties, (ii)The AR (accuracy ratio)
value of the simple multivariate regression model developed in this study which explains the transition ofrooms from vacancy to occupation takes about 0.4 and (iii)this transition probability of the model is mainly
explained by an age of a building.
Keywords. Web data, Apartment loan, Multivariate analysis, Real estate, Comparison between databases.

1. Introduction

study of individual room occupation in Japan has

The apartment loan is a loan for rentals such as

been given by Kobayashi [1]. In the paper, hemade the model which describe the periods ofcondos and apartments. This loan is a very largethat rooms are empty by using the data of theloan which is the account for a percentage ofoccupation of rooms provided by three realmore than 10 percent of the whole banks' loan inestate agencies (673 rooms).

Japan. However, a risk model of the apartment

simulated earnings based on results of dataloan with the appropriate accuracy has not been

analysis.

provided mainly due to the lack of database of

The

main

difference

between

Kobayashi’s study and our study is that we use

the occupation of Japanese rental rooms. There

the web data, which are open to the public, large

are difficulty in making nationwide the databasescale and easily scalable, but are expected toof occupation by investigators in a traditional

have sampling biases.

manner to cost an excessive amount of money ortime.

Moreover, heThus, in this study, we examine the3. Data set

possibility of construction of database of theoccupation of rental rooms for the apartment

We use the two types of data sets for our study:

loan by using the housing information website(i) the survey data which is made by the realdata.

estate appraiser (Survey data) and (ii) the housing
information website data (Web data) once every

2. Related work

three months.

3.1 Survey data

There are few studies in which discuss theoccupation of Japanese rental rooms based on
the individual room data. The rare example of105

Survey data is made by real estate appraisers.

line

indicatesthecumulative

probability

They investigate rooms in a certain area

distribution of the duration of empty (days) for

obtained by stratified sampling method fromWeb data, the red dashed line for survey data andthe room lists provided by a certain bank once

the green dotted line indicates the exponential

every three months (4,333 rooms). In this

distribution.

investigation, they visit the rooms and check

4.2 Multivariate analysis

whether rooms are empty or not and the

The AR (accuracy ratio) value of the simple

conditions of rooms such as cleanliness and fees

multivariate regression model (i.e., the logistic

within the possible.

regression model) developed in our study which

3.2 Web dataexplains the transition of rooms from vacancy to

We obtain the Web data from housing

occupation takes about 0.4. In addition, thisinformation website data by the web crawlingtransition probability of the model is mainly

on 8th, 18th and 28th of each month. We use

explained by an age of a building.

the data from 11/2014 to 7/2016 and focused oncorresponding area to the survey data. This data

5. Conclusions

contains various information of rooms such as a

In this paper, in order to check the usability of

distance from a station, a dimension of a room,

web data for the risk model of the rental homesuper markets, ages of a room, a number offinancing, we analysed the two types of data:(i)stories of building etc (35,807 rooms).

Survey data and (ii) Web data. By comparison
between two data, we found that the web data is

4. Results

approximately corresponding to the survey data

In this section, we present the summary of results

which was made by experts with respect toobtained by analysis of above-mentioned two

statistical properties. In addition, we showed thattypes of data.

web data has ability of prediction of occupation

4.1 Sampling bias of web dataof rooms by using the simple multivariate

By comparison of between survey data and web

regression model.

data,

we found that the web data hasapproximately

corresponding

References

statisticalproperties to the survey data which was made byexpertsandsampling).

sampledAccurately,

randomly
however,

1. Kobayashi, S., A Study about How to Estimate(stratified

Future-Cash Flow in Real Estate Evaluation
with Micro-Analysis of Move-in and Move-out.
Financial Planning Kenkyu, 16, 18–27, 2016
(in Japanese)

thiscorrespondence is valid for the statistics forbuildings, but is not valid for the statistics forrooms.

Figure 1 shows the example of theagreement of probability distribution between
survey data and web data. Here, the black solid
Figure 1. The cumulative probability distribution of theduration of empty (days) between survey data and web
data.

106

Multidisciplinary Study of the Earth’s Environment in 18th-19th
Centuries - A Trial to find an Approach to the Open Data andOpen Science
Takashi Watanabe1*1* World Data System International Programme Office, c/o NICT, Koganei 184-8795, Japan
Email: takashi.watanabe@icsu-wds.org

Summary. In data-oriented activities addressing the Open Data and Open Science, to assure extensive
multidisciplinary usage of data is crucial. However, there still exists many problems mainly caused bydifferences in “culture” of individual disciplines, particularly on Open Data issue. To specify current problems,
provisional search and usage of data are conducted by accessing various kinds of data opened on theInternet to study the environmental and economic movements in the interval of 18th-19th Centuries. It isfound that we need to pay effort to have ready-to-use datasets for scientists out of the field. Many old data
are still remained to be digitized in machine-readable formats. Special effort will be needed also to manage
data only available in publications as the forms of tables or diagrams. The ethical issue on reuse of thesedata is essential. Involving citizen scientists opening their data will be important also. By this trial, the studyof this interval is found to be a good starting point of a potential multidisciplinary research project of thecomprehensive study of the future human society under the influence of the global environmental change.
Keywords. Open Science, Open Data, Environmental Sciences, History, Economics, Climate Change.

1. Introductionof relatively cold environment and high volcanic
activities. A study of this interval will be
important to know the world just before theanthropological environmental changes became
prominent. In this research activity, we will beable to expect to collaborate with citizen
scientists because we are finding many datasets
relevant to our research as the outcomes of theirpersonal researches. From the point of view of
the Open Science, collaboration with them will behighly desired.

Although multidisciplinary data usage is very
important in the new paradigm of science, Open
Data and Open Science, this is not an easy task.
For example, the majority of conventional
databases have been constructed for usage
mainly by related domain scientists. It is still
difficult to find appropriate data for out-of-thefield researches through current search engines.
Even when one succeeded to find a dataset to be
used for his/her study, it will still be difficult to usethem without appropriate knowledge on the data
because the documentation of the data are notnecessary to be prepared thinking about
potential users outside the discipline.
To find current obstacles in multidisciplinary
usage of data, a trial of search and analysis of datais performed on environmental and economic
data covering the interval of 18th-19th Centuries.
This interval was located in the recovery phase of
the Little Ice Age, starting in the late Medieval era,and several important movements, e.g., theFrench Revolution and the Industrial Revolutions
in England, were taken place under the influence2.

Provisional Data Search and Data
Analysis

A trial to search for data was performed via
Internet by using currently available searchengines, just to be done by a user who do nothave sufficient knowledge on the data. For
example, environmental data acquired bymodern instrumental measurements can beobtained without important difficulty, a lot of
problems exist on reconstructed data basing on
old proxy records, e.g. temperature data in theinterval before the era of instrumental records.

107

Many data of this kind are published only in theform of diagrams or tables in scientific articles as
outcomes of the research works. We find similar
situation for old data on, for example, market
prices of wheat in old days. We will havetechnical and ethical issues in reuse of these data.
A part of historical data, e.g. data of prices ofmaterials at local markets and reconstructed
weather data basing on old diaries, have been
opened also by dedicated citizen scientists,
mainly via their Web pages. In the point of viewof Open Science, involving them into research
activities will be important but a mechanism ofquality assessment of these data will be needed.
For the present provisional study, several
examples of basic data showing economic andenvironmental situations in 18th-19th Centuries,
mainly in Europe, are shown in Fig. 1a-d. As ameasure of the economic activity in Europe, The
time series of the wheat price at London [1] isgiven in Fig. 1a, showing an interesting rise of
price in the interval from about 1800 to 1820. As
seen in the sunspot number data [2] given in
Fig.1b, this interval was situated in the interval of
relatively low solar activity (Dalton Minimum)
appeared in 1790-1830.
The British yearly
averaged temperature [3] shown in Fig.1c showsthat the climate in 18th-19th Centuries was
relatively cold, presumably due to the low solar
activity and a series of several intense volcanic
eruptions [4], including Laki in 1783 and Tambola
in 1815 (Fig. 1d). There were many reports on
world-wide depressions of agricultural andcommercial activities taken place in association
with these big eruptions.
Although various studies on this interesting
interval are still in progress by researchers of thehistory and the economics [5], influences ofchanges in natural environment are not treatedas important factors because big political andeconomic movements were taken place in thisinterval particularly in Europe, e.g., the French
Revolution and the Industrial Revolutions in
England, etc. Detailed multidisciplinary studies ofenvironmental and social relationship from theglobal point-of-view will open a new approach toget better understanding of movements of thehuman society.

3. Concluding Remarks
It is found that we still have many technical andethical problems to assure multidisciplinary
usage of data. To resolve these problems,
collaborations among scientists in various
research domains will be important. To stimulate
the collaboration, it will be important to have acommon interest (or a driving force) by selecting
an “attractive” research project to perform
multidisciplinary studies.
The study ofenvironmental and social connections in 18th-19th
Centuries will be a good starting point of apotential research program on the future of the
Earth under the global climate change. This
program will be attractive also for citizen
scientists.

References
1.

2.

3.

4.

5.

108

Clark, J., The price history of English
agriculture,
1209
–
1914.
http://faculty.econ.ucdavis.edu/faculty/gclark/papers/Agprice.pdf, 2003
World Data Centre for the production,
preservation and dissemination of theinternationalsunspot
number.
http://www.sidc.be/silso/datafiles, 2018
Met Office Hadley Centre Central England
Temperature
Data,
https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html, 2018
Ice Core Volcanic Eruptions Data Tables,
http://chandra.harvard.edu/edu/formal/iceco
re/Volcanic_Eruptions_Data_Tables.pdf, 2018
Freeman, C., Rouca, F., As time goes by fromthe Industrial Revolutions to the Information
Revolution. Oxford, 2001

Figure 1.
Time series of economic andenvironmental data in 18th-19th Centuries: (a)wheat price (shilling/bushel) at London [1], (b)yearly sunspot number [2], yearly mean
temperature (deg. C) in the central England [3],and the level of volcanic eruption (conductivity of
the ice core, s/cm-1 x 10-2) in the world [4].

109Data Management at Polar Research Institute of China
Lizong Wu1*, Beichen Zhang1, Jie Zhang1
1* Polar Research Institute of China, 451 Jinqiao Road, Shanghai, 200136, China
Email: wulizong@pric.org.cn

Summary. Chinese National Arctic & Antarctic Data Center (CN-NADC) established in 2003 by the State
Oceanic Administration (SOA) of China to manage and disseminate scientific data resulting from researchwithin the China Antarctic and Arctic Scientific Expedition. CN-NADC helps fulfill China’s obligations under
Article (III).(1).(c) of the Antarctic Treaty which states that "Scientific observations and results from Antarctica
shall be exchanged and made freely available. The CN-NADC responsibilities include management of
Antarctic and Arctic scientific data for the long-term making it available in an easily accessible form,
providing metadata records for all China Antarctic and Arctic scientific research data available for public
searching in an effective form, and enabling China Antarctic and Arctic Expedition data is available for SCAR,
IASC, SOOS, WDS and other international data systems. Since 2003, CN-NADC is a sub-platform of “National
Data Sharing Infrastructure of Earth Science” supported by the Ministry of Science and Technology (MOST)
and the Ministry of Finance of People’s Republic of China. In 2018, CN-NADC upgrade to one of National
Data Center which is a national data center system include about 20 data centers for all disciplinse andregions in China. The CN-NADC carry out various practice in repository management functions and related
systems and facilitates the custodian's right to be cited as the source of published data. Data publication
activities are cooperation with “Global Change Research Data Publishing and Repository”. Scientists within
the China Antarctic and Arctic Expedition are responsible for complying with all aspects of the China
Antarctic and Arctic Program Data Policy, including submission of raw and processed data, derived products
and associated metadata in an acceptable form to the Data Centre within the timelines agreed for datasubmission within their Data Management Plan.
Keywords. Data sharing, Data quality manage, History data rescue, Data publication.

110Arctic Data Archive System (ADS)
Hironori Yabuki1*,2, Takeshi Sugimura2, Takeshi Terui2
1

2* Polar Environment Data Science Center, DS, ROIS, 10-3 Modori-cho, Tachikawa, Tokyo, 190-8518, Japan
International Arctic Environment Research Center, NIPR, ROIS, 10-3 Modori-cho, Tachikawa, Tokyo, 190-8518,
Japan
Email: Yabuki.hironori@nipr.ac.jp

Summary. Arctic Data archive System(ADS), through proceed with the visualization and the development of
online analysis system of integrated big data, aiming for integrated analysis information platform, not only
as a mutual distribution platform of data, we have developed a system that enables open access researchdata and scientific knowledge obtained in the Arctic research. Various applications and services developedby ADS should not be used only in the Arctic but should be used as a bipolar data publish platform. The ADS
team is currently preparing to publish not only Arctic data but also Antarctic data.
Keywords. Arctic, Global warming, ArCS, Data Management.

1. Introduction

the research results. Also those integrated with

The easy access use is made possible from thethe big data, scientific knowledge by using these,
it is necessary to continue to properly publish toindustrial and the social public using researchsociety.

results(thesis and research data, etc.) using apublic research fund, and a concept as open

2. Development

science aiming at linking it to creation ofascientifictechnical

research

Arcticeffectively is showing a rapid expanse to creation(GlobalResearch

Council),as a mutual distribution platform of data, we havedeveloped a system that enables open access

Under these background, even in Arctic research,

research data and scientific knowledge obtained

open access of a variety of variation mechanism
knowledge,

such

as

in the Arctic research.

futureADS

prediction result brought about by actual graspIn order to clarify the environmental variation

been

doing

the

systems

Metadata Management System by own
metadata schema.(KIWA: Fig.1)

system of complex Arctic with a variation of thetime-space scale that is different consisting of air-Metadata exchange system by using
OAI-PMH, GI-cat.

land- marine, and human sphere, throughresearch,hasdevelopment of following up to now.

their environment change has been demanded.

interdisciplinary

System(ADS:

integrated analysis information platform, not only

Development) and G8 in 2013 etc.

scientificarchiveanalysis system of integrated big data, aiming forOECD(Organization for Economic Cooperation and

and

Data

visualization and the development of online

research result and data by a public research fund
GRC

Data

https//ads.nipr.ac.jp), through proceed with theof worldwide. And the principle opening to thebyArcticarchive System(ADS)

innovation by opening the new way as well as
promotingof

through

Currently, this service is carried out incooperation with GCW in WMO. Alsointerdisciplinary research, a wide variety ofthis service have done coordination

observational data, simulation data, satellite data,with GEO-Portal.

and even there is a need for the creation of A New
knowledge of using the big data that integrates

111

Fig.1 : Structure of ADS, Research data registration system and Metadata search service(KIWA), Online visualization
application for Climate, Satellite and Simulation data(VISION) and Semi-real-time polar environ. obs. Monitor and Sea Ice
prediction(VISHOP)

A system for space-time search using



usage

GoogleEarth collected data


DOI

(Digital

ObjectIdentifier)

Visualization and analyzed system for theAdvancement of visualization, basic data

satellite data and grid data by online(VISION:

analysis and the like of software and Web

Fig1)

applications

System to Semi-real-time polar environ. obs.

integrated analysis platform

monitor and sea ice prediction in the Arctic,



in

order

to

providean

System construction of the push-type
information service

Antarctic by using the satellite data (AMSR2)
that is delivered in near-real-time from

Advancement of small and medium-sizeddata server linkage function by ADS grid

JAXA.(VISHOP:Fig1)


Enhanced of international cooperation ofdata and metadata

registration systemPromotion of data registration and data

System for visualizing numerical data such



System Technology publishing, which isresearch and development in the ADS andas time-series data(VISION-Graph)

technology transfer promotion to other

3. Future development and challenges

systems.

ADS is not only a system that provides the data tovarious data users and stakeholders, in order to

4. Conclusions

promoteinternational

The share of research data and scientificcooperation in the Arctic region, anyone that is

joint

researchandknowledge in the Arctic and non-Arctic nations,

aimed at developing integrated analysis platformthere are need for coordination of data repository

through the available Web interface. Furthermore

and data center in a various country.

in ADS, to developing of the information

Important to drive the open-science, it isproviding service of push-type in accordance

important data published and data cited, it iswith the needs of stakeholders.

necessary to promote these data published andBy widely publish the technology developed indata cited. We, through the development of ADS

ADS, to promote the technology transfer system

activity, believe that can contribute to the sharing

construction, to help the same technical problem

of research data and scientific knowledge in thesolved in other areas. In ADS future, to carry out

Arctic and non-Arctic nations.

research and development the following items.
Advancement

of

data

and

meta-dataregistration and retrieval system

112

DIAS Platform Contributing to Open Sciencein Earth Environmental Informatics
Masaki Yasukawa1*
1* Earth Observation Data Integration and Fusion Research Initiative, the University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan
Email: yasukawa@iis.u-tokyo.ac.jp

Summary. Data Integration and Analysis System (DIAS) is one of data centric cloud. DIAS collects andintegrates massive and diverse data such as earth observation data and socio-economic data, and various
applications on DIAS platform provide beneficial information to solve societal issues such as environmental
problems in Japan and overseas. This article gives the concept of DIAS at first. As a case study on DIAS's
contribution to open science, I introduce a data sharing system for flood forecasting in Sri Lanka, and datacollecting systems for citizen science of butterfly and oriental stork.
Keywords. DIAS, open science, data centric cloud, data sharing, citizen science.

1. Introduction

knowledge and resolve global environmental

Data Integration and Analysis System (DIAS)

and societal issues.
•

started from 2006. The goals of DIAS are to collectResearchanddevelopment(R&D)

and store earth observation data; to analyze such

community: DIAS provides a close R&D

data in combination with socio-economic data,

environment that allows domain scientists

and convert data into information useful for crisis

who study the environment and IT experts tomanagement

global-scale

promote joint research and development

environmental disasters, and other threats; and to

through cooperative work, planning, andwithrespect

toproduction.

make this information available within Japan andoverseas. The prototype of DIAS was developed

DIAS project is unique, because a community toin 2010. Then, phase II of DIAS from 2011 madesupportfurther advancement and expansion to apply

infrastructure is established in addition to the

DIAS as a social and public infrastructure. The

construction of data infrastructure.

current project of phase III has started since 2016

application

development

on

data

3. DIAS Platform

with the aim of its practical operation.

DIAS provides the platform to process the global

2. DIAS

environment data. When the domain researchers

The three systems that comprise DIAS are shown.

install their programs in DIAS, and they use real-•time data archived on DIAS, the application of

Infrastructure system: integrates Earth andreal-time processing is easily realized. The

local observation data and numerical models,applications related to an open science on thesocio-economic data, and other massive

DIAS platform are introduced as follows.

datasets relating to the global environment.
•

Application

development:

3.1 Data Sharing System for Flood Forecasting

implements

in Sri Lanka

storage, search, analysis, visualization, andotherdata-related

functionson

In Sri Lanka, a large flood occurred in late May

the2017, and many residents were sacrificed. For the

infrastructure system to provide scientificsecondary disaster precaution and the basinreconstruction, a web-based data sharing system113enhancing the collecting system of butterfly infor flood forecasting has been developed in June
2017, and the data providing has been started toApril 2018. In this activity, the feature is to identify

the local stakeholders.

the individual number of the stork. About 1,800

This system has data processing tools andrecords with photograph were collected fromvisualization tools. The data processing tools

April to August 2018. The habitat map and theinclude data collection, data correction, and real-safety confirmation list were made from thetime prediction. The visualization tools candataset. Using the location, the behaviour anddisplay various real-time data such as rain gauge

the food, the clarification of the history of life anddata, satellite data, rain map, rain forecast andpersonality will be expected in future.

flood prediction.

4. Conclusions

The local stakeholders use this system forflood monitoring. Also, this system is useful for

DIAS is not only a data repository, but also anteaching material for technology transfer. For

application platform. The field of the application

more effective flood prediction in future, they willon DIAS platform is wide range. Also, some ofconsider the suitable flood prediction model, andapplications are contributing to open science forconstruct the new system themselves.

earth environment.

3.2 Data Collecting System for Citizen Sciencesystems for citizen science are the practicalof Butterfly

Especially, data collecting

examples of open science. By collecting more

The butterfly is familiar with citizen, and suitable

data

as the index of global warming and urbanization.

developing the visualization tools, a lot of new

A data collecting system for citizen science ofof butterfly andoriental

stork

and

findings on ecology will be expected in future.

butterfly in Tokyo has been constructed in 2009.
The participant uses the data upload tool, andAcknowledgments. This study received theuploads the monitoring data with photograph.

support of the Ministry of Education, Culture,

Data manager of butterfly expert uses the data

Sports, Science and Technology study trust

quality control tool, and do data cleansing. Afterbusinessthe cleansing, the data is open to the public byPlatform Development & Promotion Program

data visualization tool.

(DIAS-PF)".

"GlobalEnvironment

Information

In 9 years, about 50,000 records were
uploaded. A lot of findings such as theReferences

distribution expansion of southern butterfly, the1.

distribution expansion of alien species, andexistence of the species on the red-list werebrought by the power of the citizens.
3.3 Data Collecting System for Citizen Scienceof Oriental Stork2.

The last one of wild oriental stork in Japan wasdied in 1971, due to farmland consolidation,
disappearance of wetlands by river refurbishment,
and the use of pesticides. By the activity of feral
population reproduction with breeding andreleasing, about 100 oriental storks inhabit
satoyama landscape in Japan now.

3.

For contributing to the monitoring activity of4.

oriental storks, a data collecting system for citizen
science of the stork has been developed by

114

Kawasaki, A., Yamamoto, A., Koudelova, P.,
Acierto, R. A., Nemoto, T., Kitsuregawa, M.,
Koike, T., Data Integration and Analysis
System (DIAS) Contributing to Climate
Change Analysis and Disaster Risk Reduction.
Data Science Journal, 16, 41, 1-17, 2017
Yasukawa, M., Ikoma, E., Nemoto, T., Rasmy,M., Tsuda, M., Ushiyama, T., Tamakawa, K.,
Koike, T., Kitsuregawa, M., Prototyping a Data
Sharing System for Flood Forecasting: A Case
Study on Sri Lanka,
3rd International
Symposium on Big Data Analytics in Scienceand Engineering (BASE 2017), 2017
Ikimoni, http://butterfly.diasjp.net/[accessed on: September 2018] (in Japanese)
CitizenScienceofOriental
Stork,
https://stork.diasjp.net/[accessedon:
September 2018] (in Japanese)An attempt for the thermal transport modelling of fusion
plasmas based on the statistical approach
Masayuki Yokoyama 1*, 2
1* National Institute for Fusion Science (NIFS), National Institutes of Natural Sciences (NINS)
2SOKENDAI (The Graduate University for Advanced Studies)both at 322-6 Oroshi, Toki, Gifu 509-5292
Email: yokoyama@nifs.ac.jp

Summary. A statistical approach has been attempted for the thermal transport modelling for plasmas infusion experiment [1]. This approach has become possible with the analysis database accumulated by theextensive application of the integrated transport analysis suite, TASK3D-a [2], to the LHD (Large Helical
Device) experiment at National Institute for Fusion Science (NIFS) [3]. After the first attempt that was
published in Ref. [1], the validity check of the obtained regression expression for the ion heat diffusivity
profiles has been made to be compared with several LHD experimental results. The first attempt [1] and thesuccessive progress will be reported in the workshop, as one of possible and innovative “data-driven”
approaches in fusion research.
Keywords. fusion experiment, Large Helical Device (LHD), TASK3D-a, thermal transport modelling, statistical
approach.

1. Introduction

Recent development of TASK3D-a, and its

Conventionally, scaling laws for the global energy

extensive application to a wide-ranging LHD
plasmas have created the transport analysis

confinement time (τE) have been one of thedatabase which includes profile information suchapproaches to systematically grasp the energy
confinement

propertyof

as ion and electron temperatures (Ti and Te),fusion-experimentelectron density (ne), heating deposition, and ion

plasmas, and are also considered as one of theand electron heat diffusivities (χi and χe), andguidelines to design/predict future device.

others.

On the other hand, physics-based transport

The accumulation of TASK3D-a analyses

models have been employed to predict theplasmaperformance,

results has led to the attempt at deducing

as

expectedcertainoperation

regression analysis for the ion heat diffusivity, χi,

scenarios. However, it should be recognized thatwith certain parameters based on a statisticala satisfactory prediction in a short time scale has

approach. Here such data are collected from so-

not been realized.

called high-Ti scenario [4] in LHD experiment.

temperature

profilessuch
for

In this poster presentation, an attempt to

On performing statistical analysis, χi isovercome such difficulties will be reported, based

dimensionally normalized by Bohm diffusionon the statistical approach utilizing analysis

coefficients, Ti/(eB). Candidate predictive variables

database created by the extensive application ofare also made into dimensionless variables, suchTASK3D-a to LHD plasmas.

as,normalizedioncollisionality(νi*),

thenormalized ion Larmor radius (ρi*), and thetemperature ratio (Te/Ti).

2. Regression analysis based on
TASK3D-a database

Here, as a standard exercise in scaling studies,
the assumed simple power-law scaling model has
been transformed to the log-linear form. Multiple

115

ordinary least squares (OLS) regression analysis

transport “model.” The validating calculations

has resulted in the regression expression forhave shown promising characteristics, which will

χi/[Ti/(eB)]. An important statistical measure of thebe reported in the workshop.

2

quality of the model is the ratio R of the variation
Acknowledgments.

explained by the model to the total variation. The
2

The

authorappreciates

obtained value is R =0.83, which is a relatively

valuable advices on statistics from Prof. K.

high value, indicating that the above equation

Shimizu and Prof. Y. Iba (The Institute of Statisticalreasonably reproduces the response variable. The

Mathematics, ISM). Dr. H. Yamaguchi (Nationalroot-mean-square-error (RMSE) value is 0.28.

Institute for Fusion Science) is also acknowledged

The validity check of the obtained regression

for his contributions on the validity check of thisexpression for the ion heat diffusivity profiles has

approach. This work has been supported by the

been made through its implementation into the

NIFS

predictive TASK3D calculations to be compared

NIFS14KNTT025,

with several LHD experimental results. It has

NIFS18KNTT046, and by the ISM Collaborative

indicated

Research Program, H30-1002.

thepromisingcharacteristics

forCollaborative

Research
NIFS14UNTT006

Programs,
andreproducing the ion temperature profiles with areasonableagreement

withReferences

experimental

observation.

1.

3. Conclusions

2.

A statistical approach is proposed as the thermaltransport“modelling”

infusion3.

plasmas.

Statistically-confident regression expression for4.

the ion heat diffusivity for LHD high-Ti plasmas
has been obtained, which can be directly
implemented into the predictive simulation as a

116

Yokoyama, M., Plasma and Fusion Research, 9,1302137, 2014
Yokoyama, M., et al., Nuclear Fusion, 57,
126016, 2017
Takeiri, Y., IEEE Transaction on Plasma Sciences,46, 1141, 2018.
Nagaoka, K., et al., Nuclear Fusion, 55, 113020,
2015

International Workshop on Data Science- Present & Future of Open Data & Open Science –AUTHORS INDEX

Mishima Citizens Cultural Hall &Joint Support-Center for Data Science Research,
Mishima, Shizuoka, Japan
12–15 November 2018

Authors Index :(Alphabetical Order; First Author Only; Indicate ("Poster" Presentation-Number))
Authors Name

Page

Andres, Frederic -------------------------------------------------------------------- 17-19
Aoki, Takaaki------------------------------------------------------------------------- 20-21
Arita, Masanori --------------------------------------------------------------------- 22-23
Choi, Myung-Seok ------------------------------------------------------------------ 24-25
Furukawa, Kazumi ------------------------------------------------------------------ 26-28
Goto, Susumu ----------------------------------------------------------------------- 29-30
Hayashi, kazuhiro ------------------------------------------------------------------ 31-32
Ito, Shinsuke ------------------------------------------------------------------------- 33
Joo, Dongchan ---------------------------------------------------------------------- 34-35
Kadokura, Akira ---------------------------------------------------------------------36-37
Kadokura, Akira (P-5)---------------------------------------------------------------38-39
Kaminuma, Eli ----------------------------------------------------------------------- 40-41
Kanao, Masaki (P-6)---------------------------------------------------------------- 42-43
Kanao, Masaki (P-7)---------------------------------------------------------------- 44-45
Kato, Fumihiro ---------------------------------------------------------------------- 46-47
Kato-Nitta, Naoko ------------------------------------------------------------------ 48-49
Kitamoto, Asanobu ---------------------------------------------------------------- 50-52
Klump, Jens -------------------------------------------------------------------------- 53-54
Koo, Hearan ------------------------------------------------------------------------- 55-56
Kurakawa, Kei ----------------------------------------------------------------------- 57-59
Lewis, James ------------------------------------------------------------------------- 60-61
Ma, Juncai ---------------------------------------------------------------------------- 62
Minami, Kazuhiro ------------------------------------------------------------------- 63-64
Mwitondi, Kassim S. ---------------------------------------------------------------- 65-69
Nakamura, Takashi ----------------------------------------------------------------- 70-71
Nakano, Shin'ya (P-10)------------------------------------------------------------- 72-73
Nishimura, Koji (Session C, P-4)-------------------------------------------------- 74
Nose, Masahito ---------------------------------------------------------------------- 75-76
Ritschel, Bernd ----------------------------------------------------------------------- 77
Roy, Chandra Shekhar(P-8)-------------------------------------------------------- 78-80
Saito, Masaya M. -------------------------------------------------------------------- 81-82
Shimizu, Atsushi --------------------------------------------------------------------- 83
Singh, Shailza ------------------------------------------------------------------------- 84-86
Storchak, Dmitry A. (Session A) -------------------------------------------------- 87-88
Storchak, Dmitry A. (Session C) -------------------------------------------------- 89-90
Suzuki, Shizuo ------------------------------------------------------------------------ 91-92
Taguchi, Satoshi --------------------------------------------------------------------- 93-94
Takeda, Hideaki ---------------------------------------------------------------------- 95-96
Tanaka, Yoshimasa (Session D, P-2)---------------------------------------------- 97-98

117

Authors NamePage

Toda, Yuko ---------------------------------------------------------------------------- 99-100
Tsuboi, Seiji --------------------------------------------------------------------------- 101
Wang, Juanle ------------------------------------------------------------------------- 102-104
Watanabe, Hayafumi (P-1)--------------------------------------------------------- 105-106
Watanabe, Takashi ------------------------------------------------------------------ 107-109
Wu, Lizong ----------------------------------------------------------------------------- 110
Yabuki, Hironori (Session E, P-3)-------------------------------------------------- 111-112
Yasukawa, Masaki -------------------------------------------------------------------- 113-114
Yokoyama, Masayuki (P-9) --------------------------------------------------------- 115-116

118